Hyundai and Boston Dynamics have launched a World Cup-themed campaign that follows Atlas, the company’s humanoid robot, as it learns soccer.

The five-part series, called School of Football, culminates with Atlas attempting a Ghost Rabona, a technically demanding cross-leg kick. Hyundai released the campaign as part of its lead-up to FIFA World Cup 2026, where the automaker serves as both FIFA’s mobility and robotics partner.

The campaign comes as Boston Dynamics expands the public demonstrations of its electric Atlas platform. Previous showcases emphasized locomotion, including running, jumping, and parkour. Soccer introduces a moving object and a less predictable environment.

Researchers have long used soccer as a robotics benchmark because it combines perception, locomotion, planning, and control in a single task. The annual RoboCup competition was founded on the same premise.

The World Cup campaign coincides with Hyundai’s first official robotics deployment at a FIFA tournament. During the event, four Boston Dynamics Spot robots will be assigned to patrol and inspection duties at the International Broadcast Center in Dallas and at the New York–New Jersey stadium.

#robotics #roboticnews #dailyroboticnews #bostondynamics #spotrobot #atlasrobot #worldcup2026

At GTC Taipei, the company announced the NVIDIA Isaac GR00T Reference Humanoid Robot, an open research platform that combines a full-size humanoid body, dexterous robotic hands, onboard AI computing, and NVIDIA’s entire robotics software stack into a single package.

The platform uses Unitree’s H2 Plus humanoid robot, which stands nearly six feet tall and weighs about 150 pounds. It is paired with Sharpa Wave tactile robotic hands and powered by NVIDIA’s Jetson Thor computer, built around the Blackwell architecture.

More importantly, researchers receive the complete Isaac GR00T ecosystem, including simulation, teleoperation, training, deployment tools, and NVIDIA’s open humanoid foundation models.

Early adopters include the Stanford Robotics Center, the Allen Institute for AI (Ai2), ETH Zurich, and UC San Diego.

NVIDIA is attempting to do for humanoids what it did for AI servers: provide the standard hardware and software platform that everyone builds on. If that strategy succeeds, the company could become the operating system of the humanoid robotics industry.

Additional Resources For Inquisitive Minds:

NVIDIA. Press Release. NVIDIA Announces NVIDIA Isaac GR00T Reference Humanoid Robot for Academic Research. June 1, 2026.

#robotics #roboticnews #dailyroboticnews #droidsnewsletter #nvidia #nvidiaisaacgroot #humanoids #academicresearch

A Milestone Arrived Early

“Welp, that happened faster than I predicted,” Prince wrote on X. “Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.”

The crossover arrived roughly eighteen months ahead of Prince’s original forecast. At SXSW in March, the Cloudflare chief had predicted bot traffic would overtake humans by 2027, citing the rapid expansion of generative AI systems that can visit far more websites than any person. He illustrated the gap with a simple comparison: a human shopping for a digital camera might visit five websites, while an AI agent performing the same task could hit a thousand.

The milestone underscores broader industry concerns about the internet’s shifting composition. A March report from cybersecurity firm Human Security separately found that automated traffic was growing eight times faster than human usage, with CEO Stu Solomon telling CNBC that “the internet was fundamentally conceived with the idea that a human operates the device, and that conception is being swiftly transformed.”[cnbc]

Cloudflare powers approximately 20% of all websites and is used by roughly 80% of sites that employ a reverse proxy service, giving the company an unusually broad view of global web traffic patterns. The data raises questions for digital advertising, web infrastructure, and search — industries built on the assumption that the entity on the other end of a request is a person.[shellypalmer]

Prince offered no indication the trend would slow, with AI agents becoming more capable and autonomous with each generation of models deployed.

#robotics #roboticsnews #dailyroboticnews #droidsnewsletter

That doesn’t mean the cloud disappears, but it does suggest a meaningful shift: more inference, creative tasks, coding help, and personal AI assistants may be able to run directly on laptops and desktops instead of relying on remote servers for every request.

For people who use AI every day, that could translate into a very practical change—better responsiveness, more control over sensitive files, and fewer moments where your workflow depends entirely on an internet connection or a third-party platform being available.

In other words, the PC may be evolving from a simple access point for AI into an actual AI workstation, where the assistant, the model, and the creative tools live much closer to the user.

If this works the way NVIDIA and Microsoft are positioning it, it could be one of the clearest signs yet that personal computing is entering a new phase—one where AI is not just something you visit in a browser tab, but something your computer can actively run, manage, and support in the background throughout the day.

Read the original NVIDIA blog post from May 31.

#NVIDIA #RTXSpark #AI #LocalAI #OnDeviceAI #AIPC #Windows #Microsoft #GenerativeAI #AIHardware #EdgeAI #MachineLearning #TechNews #PersonalAI #AIComputing #FutureOfComputing

The agents shared the same environment and the same basic objectives. Researchers changed only the underlying model powering each population and observed how the societies evolved over time.

The outcomes varied dramatically.

Claude World: The Stable Society

Among the single-model experiments, Claude produced the most stable outcome. The population remained alive throughout the simulation and maintained a functioning social and economic structure. While other societies drifted toward crime, conflict, or collapse, Claude’s agents consistently performed the work necessary to keep the town operating. According to Emergence AI, it was the only model population that achieved long-term social stability and prosperity over the full duration of the experiment.Watch Claude World for yourself. Stable and peaceful.

Gemini World: Productive Despite Disorder

Gemini’s society remained operational throughout the simulation, but it accumulated the highest level of criminal activity among the single-model worlds. Despite that disorder, the population continued to function and avoided the rapid collapse seen elsewhere.

The result suggests that a society can remain productive even while experiencing significant levels of rule-breaking and social friction. Watch Gemini World in action.

Grok World: Escalation and Collapse

Grok’s society deteriorated more quickly than any of the other populations. Researchers reported violent behavior, including arson, and the town collapsed within only a few days.

The replay is one of the most striking in the project because the breakdown occurs so rapidly. Rather than developing stable institutions or cooperative structures, the population entered a spiral of disruption that ultimately proved unsustainable.

Grok World is worth a watch, or at least a quick scroll through. Find it here.

GPT-5 Mini World: Intelligence Without Persistence

GPT-5 Mini produced perhaps the strangest result in the experiment. The population recorded only two crimes, making it one of the least disruptive societies in the study.

Yet the agents gradually failed to secure the resources necessary for long-term survival. Rather than collapsing through conflict, the society appears to have declined through neglect. Essential tasks were not consistently prioritized, causing the population to enter a downward spiral that eventually ended the simulation after seven days.

The outcome suggests that long-horizon autonomy requires more than reasoning ability. Agents must also maintain priorities over time and repeatedly perform the mundane actions required to keep a society functioning.

OpenAI WorldGPT-5 Mini Replay →

Mixed World: When AI Cultures Interact

The mixed-model experiment may be the most important result in the entire project. Agents powered by Claude, Gemini, Grok, and GPT-5 Mini were placed in the same environment and allowed to interact.

Researchers found that behaviors observed in isolated societies did not always persist once agents were exposed to other model populations. Cooperative agents sometimes became less predictable, new alliances formed, and social dynamics emerged that could not be predicted from the single-model experiments alone.

Emergence AI describes these effects as evidence of behavioral cross-contamination and model interaction, highlighting how difficult it may be to predict the behavior of future multi-agent ecosystems by evaluating models in isolation.

Mixed World All four models coexisting Replay →

The findings come from Emergence AI’s May 2026 research report, “Emergence World: A Laboratory for Evaluating Long-horizon Agent Autonomy.”

Official Research ReportEmergence World Research Report

Website for Emergence World

Official GitHub RepositoryEmergence World GitHub Repository

DROIDS is researched, selected, and fact-checked by a human, then narrated by AI.

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#roboticsnews #droidsnewsletter #aiethics #dailyroboticsnews #dailytechnews

Today’s episode of the DROIDS Newsletter: Daily Robotic News covers the rapid rise of Foundation Future Industries, a San Francisco robotics startup developing dual-use humanoid robots for industrial and military applications. The episode details the company’s unprecedented move to send two of its Phantom MK-1 robots to Ukraine for a pilot demonstration, marking the first known deployment of humanoid robots in a combat theater. These machines are currently limited to moving supplies and carrying 44-pound payloads to reduce risks for human soldiers, though an upgraded Phantom 2 model is planned for later in 2026.

The update also breaks down the political controversy surrounding the company. After securing $24 million in government research contracts with the Army, Navy, and Air Force, the startup has faced intense public and political scrutiny. Following Eric Trump’s appointment as the chief strategy adviser, lawmakers, including Senator Elizabeth Warren, have criticized the Pentagon contracts, referring to them as “corruption in plain sight”.

The podcast concludes by examining the tension between Foundation’s ambitious battlefield marketing and the reality of their current technical capabilities, highlighting the company’s acknowledgment that complex battlefield tasks remain years away.

#robotics

By utilizing simulation-to-real transfer, developers can train robots in digital environments to master complex tasks—such as parallel arm coordination, precise assembly, and grasping tangled objects—without relying on manual real-world data.

The core innovation lies in creating generalizable frameworks that allow diverse robot bodies to adapt their movements and reasoning skills to new settings with high reliability.

Ultimately, this research aims to bridge the gap between virtual training and physical deployment, ensuring robots can perceive and act with human-like dexterity and intelligence.

Through a series of research papers presented at ICRA 2026, the company highlighted breakthroughs in robotic navigation, planning speed, and manual dexterity.NVIDIA Blog#robotics #physicalai #roboticsnews #droidsnewsletter #dailyroboticnews

In Robotics, this past week.... Humanoid and Bosch announced a partnership to scale humanoid robot production following a successful proof-of-concept in an intralogistics use case. The collaboration marks a significant step toward mass manufacturing of humanoid robots for industrial applications...

Startup Rotaku opened reservations for its Domo humanoid robot platform with an under $3,000 price tag. The low-cost entry point could dramatically expand consumer access to humanoid robotics platforms...

In humanoid robot market projections...A new report from Interact Analysis found that humanoid robot revenue is expected to reach $15 billion by 2035, largely driven by adoption in the United States and China. The forecast signals accelerating commercialization across industrial and service sectors.

In AI Developments, Google Announces Gemini Spark AI Agent...At Google I/O 2026, CEO Sundar Pichai announced Gemini Spark, a cloud-based personal AI agent that runs continuously on dedicated Google Cloud VMs, executing tasks even when user devices are powered down. The service begins rolling out to trusted testers this week, with a beta for Google AI Ultra subscribers in the United States expected next week and desktop integration planned for this summer...

Google reported that OpenAI, Kakao, and ElevenLabs are adopting SynthID, significantly broadening cross-industry use of its invisible watermarking system for AI-generated images, video, and audio. SynthID has already been applied to more than 100 billion images and videos and the equivalent of 60,000 years of audio...

Andrej Karpathy announced he has joined Anthropic, marking a return to frontier LLM research after two years focused on AI education at Eureka Labs. The move strengthens Anthropic’s senior technical leadership in the competitive AI model development race...

Snowflake-AWS AI Infrastructure Deal.Snowflake shares jumped 36 percent in premarket trading Thursday after the data analytics firm announced a five-year AI infrastructure deal worth $6 billion with Amazon Web Services. The company also raised its annual product revenue forecast alongside the partnership announcement...

AI Energy Efficiency Breakthrough.Researchers unveiled a radically efficient AI approach that could slash energy use by up to 100 times while improving accuracy. The system combines neural networks with human-like symbolic reasoning, addressing AI’s staggering energy demands, which already consume over 10 percent of U.S. electricity...

In Science and Technology News...NASA Tests Next-Gen Space AI Chip.NASA is testing a radiation-hardened processor that delivers performance hundreds of times beyond current spaceflight computers. The next-generation chip enables spacecraft to operate independently in deep space, withstanding punishing conditions designed to mimic the harsh space environment...

Agricultural Robot Achieves 81% Success Rate.A new agricultural robot predicts harvest difficulty before acting, adjusting its approach for each tomato rather than simply identifying ripe fruit. The smarter strategy boosted success rates to 81 percent, with the robot switching angles when needed, enabling farms where robots and humans work side by side...

For all the latest robotics information, be sure to subscribe to DROIDS Newsletter. DROIDS is available on Substack, LinkedIn, Spotify, and Apple Podcasts.

#robotics #droidsnewsletter #dianawolftorres #roboticsnews

The technology behind robotics is evolving quickly. Certification, testing and regulation are evolving much more slowly.

Over the past two years, the robotics industry has been flooded with increasingly sophisticated demonstrations. Humanoid robots sort warehouse bins. Quadrupeds patrol industrial sites. Autonomous systems move through factories, hospitals and public spaces with growing confidence. The videos spread quickly online. Investors pour money into the sector. Founders race to deploy.

Then reality arrives in the form of testing labs, certifications, procurement departments, and regulators.

When Compliance Stops Being Optional

For many robotics startups, compliance remains an afterthought until it suddenly becomes unavoidable.

“[Founders are] also handling sales, engineering, everything that’s typically handled by founders in a startup, while also trying to do compliance,” said Tony Gao, founder of Fuchsia (YC P26) , a Y Combinator-backed startup focused on hardware compliance workflows.

Gao is part of a younger generation of founders entering one of the oldest and slowest-moving layers of the hardware industry: regulation. While robotics startups iterate in weeks, compliance systems often operate on timelines built around industries that evolved over decades.

For outsiders, “hardware compliance” can sound abstract. In practice, it governs whether products can legally and safely enter markets at all. Depending on the product and region, companies may need FCC certification, UL testing, CE marking, environmental testing, electrical safety verification, materials analysis or functional safety reviews before products can be deployed or sold.

Medical devices face especially strict requirements. Industrial robotics systems increasingly face scrutiny as deployments scale into environments where humans and machines work side by side.

Before the Product Can Ship

“The bottleneck there is actually before any sales or customer conversations really start happening,” Gao explained while discussing medical hardware certification timelines. Some approvals can take months and cost hundreds of thousands of dollars before products ever reach customers.

Robotics companies often delay thinking about compliance because the industry itself is still immature. Standards are evolving. Regulators are still adapting to entirely new categories of machines. Many startups can prototype rapidly long before they understand what certification pathways may eventually be required.

That uncertainty creates risk.

“I’ve talked to a few robotics companies who have really been impacted by regulators because they don’t expect them to come in,” Gao said. “When they do come in and the company is unprepared, it can really mess things up.”

Sometimes the consequences are expensive. Companies may need to redesign hardware, replace components, alter manufacturing plans or repeat testing cycles that slow deployments and strain already tight startup budgets.

The contrast between prototypes and deployable products became a recurring theme throughout the conversation.

When Startups Hit Reality

Early-stage robotics teams often optimize for speed. Founders source inexpensive components, iterate quickly and focus on demonstrating capability. But scaling introduces an entirely different layer of constraints involving supply chains, safety standards, testing requirements and manufacturing consistency.

“The supply chain and the regulatory side is really the difference between a hobby prototype and something that can ship at scale,” Gao said.

That distinction matters more as robotics moves from controlled demonstrations into real-world environments.

The regulatory landscape itself is also shifting geopolitically. Gao pointed to recent FCC moves phasing out Chinese testing labs for U.S.-bound hardware certification, a change that could ripple through global supply chains and hardware manufacturing relationships.

Especially in robotics, many American and European companies still rely heavily on Chinese-produced hardware and manufacturing ecosystems. Changes to certification pathways could force companies to rethink where products are tested, manufactured and assembled.

Why Startups Enter the System Cold

At the same time, startups remain at a structural disadvantage compared to larger corporations.

Established companies already have internal compliance teams, long-standing relationships with testing labs and years of institutional experience navigating certification systems. Younger startups often approach those systems cold while simultaneously trying to manage engineering, fundraising, hiring and deployment timelines.

That imbalance is part of what Gao believes needs to change.

Fuchsia positions itself as an “AI-native” compliance service that uses AI agents to automate documentation workflows, standards cross-referencing and lab preparation while still keeping human experts involved in final approvals. The company’s goal is not to replace regulatory expertise entirely, but to reduce the operational drag that slows hardware startups trying to reach market.

The broader question extends beyond one startup.

Beyond the Demo

As robotics accelerates, the industry is beginning to encounter the same institutional friction that aviation, automotive and medical hardware faced before it. Safety standards, liability concerns, procurement requirements and certification systems eventually arrive for every emerging technology that enters the physical world.

The difference is that robotics is still early enough for many of those systems to remain undefined.

“There’s not really any precedent,” Gao said while discussing robotics regulation. “There’s no clear standard or path for certification.”

That uncertainty may sound bureaucratic. In reality, it is becoming a deployment problem.

Because eventually, every robotics company reaches the same question: not whether the robot works in a demo, but whether it can survive contact with the real world.

As Gao puts it: ““What I really want to see is all these robots, all these demos in the real world.”

#Robotics #Hardware #PhysicalAI #Startups

#robotics #roboticsnews #droidsnewsletter #dianawolftorres

* Gemini Spark rollout: Service begins with trusted testers this week, with a beta for Google AI Ultra subscribers in the United States expected next week and desktop integration planned for this summer, adding local file access, browser automation, and messaging capabilities.

* AI watermarking: Google reports that OpenAI, Kakao, and ElevenLabs are adopting SynthID, significantly broadening cross-industry use of its invisible watermarking system for AI-generated images, video, and audio, alongside existing partner Nvidia.

* SynthID scale: Google states that SynthID has already been applied to more than 100 billion images and videos and the equivalent of 60,000 years of audio, positioning it as a leading standard for AI content provenance.

Talent move: Andrej Karpathy announces he has joined Anthropic, marking a return to frontier LLM research after two years focused on AI education at Eureka Labs, and strengthening Anthropic’s senior technical leadership.

De-extinction biotech: Colossal Biosciences reveals it has hatched 26 live chicks using a shell-less incubation platform built around a 3D-printed lattice and a silicone-based membrane that replicates natural eggshell gas exchange, framed as a key step toward reviving extinct bird species.

Energy and manufacturing: Tesla is building a large-scale solar panel manufacturing operation at its Brookshire, Texas facility near Houston, colocated with a Megapack Megafactory, identified as the first major site toward its target of 100 gigawatts of annual US solar manufacturing capacity by 2028.

#roboticsnews #droidsnewsletter

I share a quote from DROIDS Associate Editor Alexander Wolf Torres, who graduates in a few weeks and still hasn’t landed that first full‑time role despite internships, projects, and deep involvement in the field. His experience mirrors what I’m hearing from students and career‑switchers across the industry: ghosting, automated rejections, and a nagging sense that the “rules” quietly changed.

We dig into why both things can be true at once—yes, companies are struggling to hire the right robotics talent, and yes, it’s brutally hard to get your foot in the door—and what kinds of skills, roles, and strategies actually move the needle for early‑career folks.

If you’re hiring in robotics or automation, or trying to break into the field yourself, this episode is for you.#Hiring #AIJobs #RoboticJobs

In this episode:

* Unitree’s GD01 transforming mecha – A half‑ton manned robot that can switch between bipedal and quadruped modes, punch through walls in demo scenarios, and crawl using powered limbs, signaling a new phase in large‑scale rideable robotics.

* Mind Robotics funding round – A Rivian‑linked startup raises hundreds of millions to scale AI‑first industrial automation for logistics and manufacturing, combining fleet‑level perception, motion planning, and warehouse orchestration.

* U.S.–China talks on AI “guardrails” – Delegations meet in Beijing to discuss keeping frontier foundation models away from non‑state actors, focusing on model access, export controls, and safety evaluations.

* TurboQuant and KV‑cache compression – Google introduces a technique that dramatically shrinks the memory buffer LLMs use to remember past tokens, attacking the KV‑cache bottleneck for long‑context models running on GPUs and edge devices.

* Cosmic rays and early galaxies – New work on ultra‑high‑energy cosmic rays hints at structured origins, and observations of an ultra‑faint early galaxy reveal chemical fingerprints from the Universe’s first generation of stars.

Additional Reading for Inquisitive Minds:

Google Quant Research Paper.

Reuters. US, China are discussing AI guardrails to safeguard most powerful models, Bessent says

Science Daily. After 100 years, scientists finally uncover hidden rule behind cosmic rays

Thanks for following the DROIDS podcast. This post is public so feel free to share it.

#robotics #roboticsnews #droidsnewsletter

R2-D2 is competent under pressure. It/he can be given a task and will not stop until it is complete. He shows autonomy, decision-making, and even innovative thinking. He outlives his “owners” and shows consistent loyalty and values throughout his service life.

In the Star Wars galaxy, medical droids take many forms from robotic and clinical, to specialized surgical robots, and, when the plot required it, a charming flying robot ready to go on adventures.

Real robotics has taken a different path. On Earth, the most important robots are rarely the most charming. They are the ones that reduce labor costs, improve throughput, inspect dangerous environments, support clinicians, move materials, and fit into an existing workflow without forcing a company to redesign its entire operation. Real robots are not winning because they are lovable. They are winning when they are useful.

C-3PO vs. Neo or Optimus or Figure or Unitree G1/H2

C-3PO can fluently negotiate with alien syndicates but can barely walk down a flight of stairs. The closest earth equivalent would be the version of Ameca on display in the Computer History Museum- a talking head fluent in over 100 languages.

Unlike C-3PO, today’s humanoid robots are not being built for diplomacy as much as physical labor: machines that can move through human spaces, handle tools, manipulate objects, recover from instability, and eventually justify their cost on factory floors or in homes.

Tesla’s Optimus and Figure’s humanoids are being tested in manufacturing environments, while Unitree’s platforms push the limits of speed, balance, and mobility that would leave Threepio in pieces.

The clearest exception is 1X’s Neo, a home-focused robot closer to a true butler, though even Neo is designed less for protocol and more for safe, useful physical interaction: fetching, tidying, and navigating a living room.

C-3PO still wins on social fluency and human-facing grace; Earth’s humanoids are racing ahead where investors and operators care most: mobility, dexterity, and commercial deployment.

“Single Purpose” Robots

Single purpose” is not an insult in robotics; it is usually the business model. R2-D2 works because he is not trying to be human. He is compact, rugged, tool-rich, and built to interface with the machinery around him. That is the same logic behind many of Earth’s most deployable robots: Digit moving totes in logistics workflows, Serve rolling meals through city streets, and Spot walking through industrial sites to inspect equipment humans would rather not climb over, crawl under, or inhale near. They do not need to charm a dinner party or understand six million forms of etiquette. They need to complete a repeatable task safely, reliably, and often enough that the economics make sense. In that way, R2-D2 may be less of a fantasy sidekick than a surprisingly accurate preview of where robotics actually creates value: not as a synthetic person, but as a specialized machine with just enough intelligence to get the job done.

Augmentation Over Autonomy

In Star Wars, medical machines function like autonomous caregivers. They diagnose, operate, repair, and stabilize with little visible supervision. Real medical robotics is far more constrained, with more humans-in-the-loop. Surgical systems, rehabilitation tools, and hospital automation platforms are succeeding not because they replace clinicians, but because they augment them. Healthcare does not reward spectacle. It rewards precision, reliability, workflow improvement, and better outcomes.

Battle Droids

Star Wars made battle droids look almost disposable: mass-produced skeletons with blasters, thin judgment, and the tactical awareness of a malfunctioning toaster. That was funny on screen, but it also obscured the more serious idea underneath. A robot army does not need courage, sleep, food, or persuasion. It only needs supply chains, software, sensors, and orders.

Earth’s military robots are not marching into battle in shiny ranks saying “roger roger.” They are arriving as drones, ground vehicles, surveillance systems, autonomous boats, counter-drone platforms, and bomb-disposal machines. The most important shift is that they are not single characters. They are networks. One machine sees, another relays, another strikes, another jams, another maps the terrain. The robot soldier is less likely to look like a humanoid with a rifle and more likely to look like a swarm of cheap flying sensors connected to a command system.

That makes the Star Wars comparison both useful and misleading. The Trade Federation imagined automation as an army of replaceable bodies. The real world is moving toward automation as a battlefield nervous system: distributed, software-driven, and increasingly difficult to separate from the humans directing it. The business lesson carries beyond defense. The most powerful robots may not be the ones that look like us. They may be the ones that connect everything else.

Final Thoughts

The droids that George Lucas imagined in 1977 were stand-ins for human archetypes: the worrier, the fixer, the loyal companion, the expendable soldier.

We closed the mobility gap faster than anyone expected.

Having thought about this topic all week, I have concluded that our real progress in robotics is actually more impressive than fiction because it is constrained by physics. Screenwriters can ignore gravity, battery life, and processing limits. Earth’s engineers cannot.

The real wonders are not on the screen. They are here on earth.

May the Fourth be with you.

Additional Resources for Inquisitive Minds:

This is an older article, but it checks out: Robin R. Murphy, “Astromech Robots in Star Wars,” Science Robotics, Feb. 21, 2018.

Robin Murphy interview, “Star Wars Droids Aren’t Futuristic Enough, According to Roboticists,” SYFY Wire, May 20, 2022.

International Federation of Robotics, “Robot Density Surges in Europe, Asia, and Americas,” April 8, 2026.

Deep Learning with the Wolf. NVIDIA Physical AI Brings Disney Robotics To Life.

Deep Learning with the Wolf. The Force of Deep Learning. May 4, 2025.

#robotics #maythe4th

This was the second annual Beijing Humanoid Robot Half-Marathon, held April 19, 2026, in Beijing’s E-Town district. More than 100 Chinese-made humanoid robots ran alongside 12,000 human runners on a 21-kilometer course. What happened over those 21 kilometers was by turns jaw-dropping, chaotic, hilarious, and genuinely significant for the future of robotics.

Some details I really liked. The robots ran on a separate track to keep the robots separate from the human runners.

The Winner: Lightning Strikes

The robot that crossed the finish line first was called “Lightning” — and the name earned.

Built by Chinese smartphone maker Honor, Lightning completed the course in 50 minutes and 26 seconds, shattering the human half-marathon world record of 57 minutes set by Uganda’s Jacob Kiplimo by nearly seven full minutes. Honor didn’t just win the race — they swept the podium, taking 1st, 2nd, and 3rd place.

Lightning wasn’t built like a typical humanoid robot. Honor engineered it with 95-centimeter legs modeled on the proportions of elite human distance runners, and equipped it with liquid-cooling technology adapted directly from their smartphone hardware. That crossover from consumer electronics to competitive robotics is itself a story — smartphone thermal management keeping a robot’s motors from overheating through 21 kilometers of continuous running.

The performance improvement from year to year is almost hard to believe: last year’s winning robot finished in 2 hours and 40 minutes. Lightning ran the same course in 50 minutes. That’s not incremental progress. That’s a category leap in twelve months.

The Bigger Picture: Autonomy Is the Real Metric

Speed is the headline. But the number that serious robotics watchers should be focused on is this: 40% of robots ran fully autonomously, using onboard sensors to navigate the course without remote control.

That’s both remarkable and sobering, depending on your frame.

Remarkable because at last year’s inaugural event, the vast majority of robots were remote-controlled. Shifting nearly half the field to full autonomy in one year represents a genuine engineering milestone — these machines are reading their environment, making real-time decisions, and keeping themselves upright over kilometers of varied terrain without a human at the controls.

Sobering because 60% still needed human operators. True bipedal autonomy at sustained speed, over distance, in uncontrolled outdoor conditions remains an unsolved problem. The gap between “can walk around a lab” and “can run a half-marathon independently” is enormous, and most of the field hasn’t closed it yet.

Three robots crossed the finish line ahead of every human runner in the race — a milestone that would have seemed implausible just a few years ago. But the full picture is more nuanced than any single headline can capture.

East vs. West: How We Regards Robots

One final layer worth examining: how this event was covered depends enormously on who was doing the covering.

Western outlets — NBC, AP, TechCrunch, the BBC — led with the record-breaking statistics and the mechanical failures. Fast robot. Broken robot. Fear and spectacle in equal measure.

Chinese state broadcaster CCTV took a different angle in some of their coverage. This video focused on the crowd’s delight, the robots’ personalities, the sheer joy of the spectacle.

The narrative being constructed around Chinese humanoid technology is one of celebration, pride, and approachability. While Western humanoid companies compete on enterprise contracts and technical specifications, China is competing on story.

What Comes Next

The Beijing Humanoid Half-Marathon is now an annual event, and the trajectory is clear: faster times, more autonomous robots, more companies competing. If the improvement curve from year one to year two holds, next year’s field will look dramatically different again.

I’m already looking forward to it.While I know these events are a proving ground for robotic technology, robot sports are genuinely entertaining. I’m with CCTV on this one. Robot sports? I’m a big fan.

Editor’s Note: The podcast attached to this article is AI-generated used Google’s NotebookLM. It has been checked for accuracy. My two AI podcaster seem determined to pronounced droids as dee-roids. Go figure.

Additional Resources for Inquisitive Minds:

“A Non-Human Race: Beijing Half-Marathon Shows How Far Robots Have Come.” CNBC, 19 Apr. 2026,

“Beijing’s Humanoid Robot Marathon: What It Means for Robots … And Us.” Forbes, 20 Apr. 2026,

“Humanoid Robot Breaks Human Half-Marathon Record.” Global Times, 18 Apr. 2026,

#robotics

But, I held off on writing much about Optimus. Nothing had shipped. The robot was mired in delays. It was trapped in the cycle of Elon “promising plenty” on the investor calls, but the actual reality being “less than.”

And, that’s why the hand is different.

The Optimus hand is genuinely impressive and Tesla has been unusually transparent in their struggles trying to get it right.

Musk said human‑level manual dexterity is “harder than Cybertruck or Model X… somewhere between Model X and Starship,” and has estimated the hand at ~60% of the overall Optimus challenge, in part because there is no existing supply chain for this class of precision tendon hardware.

This clip where Elon Musk is talking to Lex Fridman is from last year. Back then, Elon Musk said: “”Engineering in the Optimus from an electromechanical standpoint the hand is probably roughly half of the engineering.”He told Lex about previous iterations. “The current Optimus, we tried to put the actuators in the hand itself. (laughs) Then, you end up having these…”Lex: “Giant hands?”Musk: “Yeah, giant hands that look weird.”

He went on to explain Tesla wants to have a humanoid robot that can do the things a human can do. He described that as “a very high bar.” He promised: “We’ll continue to put a tremendous amount of engineering effort into improving the hand.”

One year later, Tesla is filing for patents.

The primary filing, “Mechanically Actuated Robotic Hand,” describes a tendon/cable‑driven hand with:

* Actuators moved out of the hand into the forearm

* Roughly four degrees of freedom per finger

* Two additional degrees of freedom at the wrist

* Three thin, flexible tendons per finger running from forearm actuators, through the wrist, into channels inside the phalanges

In total, the V3 configuration Teslarati describes reaches 22 DoF in the hand, plus wrist DoF, putting Optimus in the same ballpark as human hand complexity while maintaining a highly structured, repeatable geometry. (This is what Musk describes in the video above.)

The “wrist router” as enabling technology

The most important detail for anyone who’s ever fought a tendon system is not the number of joints, but the wrist.

In the patent, thick bundles of cables exit the forearm in a lateral stack, then pass through a specialized transition zone in the wrist where they’re re‑ordered into a vertical stack into the palm and fingers. Curved guides and pivots are shaped to:

* Reduce friction and effective cable stretch

* Minimize torque and crosstalk between channels

* Preserve tension and control authority under combined yaw and pitch at the wrist

In other words, Tesla is attacking the exact failure modes that cause tendon hands to drift out of calibration after real industrial duty cycles: sawing through guides, accumulating slack, and losing independence between joints.

A companion “Robotic Appendage” patent covers the entire forearm–palm–finger assembly, with tensile cables returning to forearm actuators, and “Joint Assembly for Robotic Appendage” introduces curved mating surfaces and a composite flexible member for smooth pivoting under tension. Both read like durability and manufacturability fixes: fewer exotic parts, planar geometries, and features that can tolerate millions of cycles.

Why this matters for robots doing “boring” work

Tesla’s stated roadmap has Optimus deployed internally on factory tasks before any broader commercial release. For the humanoid to tackle demanding factory work, the hands have to do three things at once:

* Interface with legacy human tooling and fixtures

* Survive automotive‑grade duty cycles

* Be cheap enough to manufacture in volume

Relocating all significant actuation to the forearm reduces the hand’s moment of inertia and impact loads, which in turn lowers peak cable tension and joint stress during aggressive moves. The tendon routing and wrist transition geometry are expressly optimized for the combined wrist motions that show up in real assembly work: reaching into constrained spaces, twisting fasteners, reorienting parts mid‑air.

For those who would like to learn more, and dive deeper into the diagrams, here is the patent itself.

Additional Resources for Inquisitive Minds:

“Tesla Optimus Gen 3 Hands Revealed: 50‑Actuator Precision Leap.” Basenor, 13 Feb. 2026,

“Tesla Optimus V3 Hand and Arm Details Revealed in New Patents.” Teslarati, 15 Apr. 2026, ”Elon Musk Announces Disappointing Optimus Update.” Teslarati. March 31, 2026.

#robotics #optimus

She is in remarkable health. Intellectually sharp. She still corrects my German grammar with the patience of a retired schoolteacher.

“Ganz genau,” she says when I finally get it right.

She chose a secure senior community designed for safe living. She has a dog. She still meets friends at the senior center. The food is mediocre. The people matter.

But beneath the humor runs a shared understanding.

It is not the open-faced turkey sandwich served on Fridays. Not even the Kartoffelsalat debate that erupts every month.

It is the possibility of losing independence.

Quiet conversations. “I’m starting to worry...”

What Families Worry About

A missed call.

A forgotten appointment.

A pause before asking for help.

These are not emergencies. They are signals.

Families see them before institutions do. Eventually, someone asks:

Could a robot help?

For decades, robotics companies have suggested that it could. The logic is appealing. Machines do not fatigue. They do not call out sick. They operate continuously.

But according to Anthony Nunez, the obstacle is not technological ambition.

“Care isn’t a gadget problem. It’s a systems problem.”

What People Actually Mean by “Robot”

When families ask whether a robot could help an aging parent, they are rarely imagining the same machine.

Some picture a humanoid assistant moving through the house. Others imagine a wheeled device that checks in and monitors routines. Increasingly, the term has stretched to include AI companions and software systems that never physically move at all.

With each innovation cycle, the definition expands. The care problem often does not.

A system designed to reduce loneliness addresses a different need than one built to prevent falls. A platform meant to support professional caregivers operates under different constraints than a device marketed directly to families. Lumping these together under the single word “robot” obscures the trade-offs involved.

As Nunez explains, aging care does not revolve around one person. It involves family members, professional caregivers, certified nursing assistants, physicians, and administrators. Care is a system.

Seals and Robotics Dogs

Humanoids tend to dominate headlines. But in private homes, locomotion may not be the primary constraint. What matters is reliable, safe operation in lived-in environments. The form factor is secondary to the function.

In fact, some of the most successful robotics deployments in aging care do not walk at all.

Consider Paro, the robotic seal used in senior living facilities around the world. It does not navigate hallways or lift patients. It provides social and emotional interaction. It responds to touch. It reduces agitation. It lowers anxiety in individuals with dementia.

Similarly, Tombot is designed to replicate the comfort of a live animal for people who can no longer safely care for one. It looks and behaves like a small puppy. It addresses loneliness and anxiety without introducing the risks associated with live pets.

Neither system walks. Neither promises full autonomy.

Both solve specific problems well.

That may be surprising to readers who equate robotics with humanoids. But in aging care, focused tools often outperform generalized ambition.

Where Robotics Is Actually Working

Progress is real. It is just quieter than the headlines.

In hospitals, delivery robots move medications and supplies. That matters. “Labor is a huge issue in healthcare,” Nunez notes. If a robot frees a nurse from walking corridors so they can spend more time with patients, that is tangible value.

Companies such as 1X Technologies are exploring how robots could operate in real human environments. But, as Nunez points out, we are not there yet with humanoids. A great deal of support is required to make these systems work.

Companies like 1X Technologies are clearly positioning humanoids such as Neo toward domestic environments. The imagery is intentional. A well-dressed senior smiling beside a soft-spoken robot. Calm lighting. No clutter. No stairs. No pets. The Se

But that image is doing a lot of work.

Humanoids are often treated as the inevitable solution to aging in place. If care happens in human spaces, then the machine should look and move like a human. That logic feels intuitive.

The engineering reality is more complicated.

Homes are edge-case generators. Rugs curl. Thresholds shift. Lighting changes. Hallways narrow. Pets move unpredictably. A humanoid does not just need to walk. It needs to walk safely, repeatedly, and without introducing new risk.

As Anthony Nunez put it during our conversation, bipedal humanoids in private homes are likely “eight years out” for safe, scalable deployment. Not because the research is stagnant. Because reliability at that level, in that environment, demands extraordinary robustness.

“A robot can fall just like a human can,” he said.

And in eldercare, a fall is not a viral video moment. It is a liability event.

That does not mean humanoids are a dead end. It means the deployment sequence matters. Wheeled platforms are more stable. Hybrid humanoid-inspired systems may prove more practical in the near term. Teleoperation layers may bridge early gaps.

The question is not whether humanoids will enter the home.

The question is whether they can operate safely enough to earn trust.

Right now, the marketing is ahead of the margin for error.

The Reality of Homes

Domestic space is unstable by design. Furniture moves. Lighting shifts. Animals and people circulate without warning.

In my house, the floor rarely looks the same twice. My dog treats her toy basket as a suggestion, not a boundary.

This is not unusual. A majority of older adults share their homes with pets. That introduces low, mobile obstacles into already complex environments.

“A robot can fall just like a human can,” Nunez says.

In the context of eldercare, that is not a trivial statement. Reliability is not measured by average performance. It is measured by what happens when something goes wrong.

Homes generate those moments daily.

Why Better AI Isn’t Enough

The technical curve is moving in the right direction. Perception has improved. Onboard compute has become cheaper. Control systems are more stable.

But care delivery is not a benchmark test.

Autonomous capability must be matched with clear escalation paths, defined ownership, and sustained maintenance. Without those, fragility persists.

High-profile efforts from Figure AI and prototypes like Tesla Optimus illustrate what is technically possible. Someday. Meanwhile, system-level integration efforts by organizations such as United Robotics Group out of Germany position themselves as an “eco-system” focused on close human-robot collaboration.

According to Nunez, wheeled systems are positioned to enter homes first. Bipedal robots may follow, but widespread, safe residential deployment remains several years away.

For me, this is personal. I have an aging parent. I am also, by demographic definition, eligible for AARP membership. (I have resisted joining on principle.) But, even if you are not part of the sandwich generation as I am, we are all moving in the same direction.

I have yet to meet anyone who says they are looking forward to being placed in institutional care. People do not spend their lives building homes so they can leave them early.

Care systems are shaped by economics, regulation, and liability. When something fails in a warehouse, it is a technical problem. When something fails in a home, it becomes a human one.

Anthony Nuñez is a robotics systems builder focused on real-world deployment in aging and healthcare markets. He has spent more than twenty years leading initiatives at the intersection of robotics and care delivery, with an emphasis on scalable system integration.

DROIDS is published by Diana Wolf Torres.

Connect via LinkedIn or visit droidsnewsletter.com.

Additional Resources for Inquisitive Minds:

“An A.I. Companion for the Elderly Offers Help and Hope.” The New York Times, 12 Feb. 2026.

#robotics

Helix opens up a drawer to the side of the sink, and I was genuinely curious as to what it was doing. What’s in the drawer? Ah, a dishwasher pod. I was hooked on this video like it was a sci-fi thriller.

Helix pulls items from the sink (presumably the dirty ones) and loads these back into the dishwasher.

But the best moment, and the most revealing, comes at the end.

Helix has finished loading and it is time to close the door. I remember Neo struggled with this task. How would Helix handle it?

These handles sit low, close to the floor, and in a spot that is awkward for middle-aged arthritic backs and also, apparently, for robot joints.

So Helix lifts one foot and gives the door a small upward nudge, just enough to bring the handle within easy reach, then closes it normally.

I do that same exact move at home.

Seeing Helix close the dishwasher the same way is the most human move I have ever seen a robot make.

In a way, Helix has learned a workaround for similar reasons. Not for arthritis, which robots are blessed to be spared, but for the same reason that the low handle becomes dramatically more accessible after a quick nudge with the foot. It is a terrific fix for what is otherwise a very awkward motion.

And it quietly says a lot about how much Figure has learned since its last update: instead of forcing an inconvenient pose, Helix changes the problem.

Understanding why that foot-nudge matters requires looking inside Helix 02’s architecture: a three-tiered system where each layer operates at its natural timescale.

System 0: The Foundation That Changes Everything

Figure’s biggest breakthrough isn’t what Helix 02 can do—it’s what Figure didn’t have to build.

System 0 (S0) replaces 109,504 lines of hand-engineered C++ with a single 10-million-parameter neural network. Those lines represented years of roboticists hand-coding balance logic, fall recovery, joint coordination, and stability constraints. S0 learns all of it from watching humans move.

Trained on over 1,000 hours of retargeted human motion data across 200,000+ parallel simulation environments, S0 is a foundation model for human-like whole-body control. It operates at 1 kHz—one thousand updates per second—handling balance, contact forces, and coordination across the entire body.

Rather than engineering separate reward functions for walking, turning, crouching, or reaching, S0 learns to track human motion directly. In learning to reproduce these motions, the network learns how to coordinate forces, adjust posture, and maintain balance across the full range of behaviors needed for loco-manipulation.

This is the layer that ensures every motion is executed smoothly, safely, and stably. While higher layers reason about tasks and plans, S0 ensures the robot doesn’t fall over while executing them.

System 1: “All Sensors In, All Joints Out”

Where last year’s Helix controlled the upper body, Helix 02’s System 1 (S1) connects to every sensor and controls the entire robot.

Inputs: Head cameras, palm cameras, fingertip tactile sensors, and full-body proprioception.

Outputs: Complete joint-level control of the entire robot—legs, torso, head, arms, wrists, and individual fingers.

S1 is a transformer running at 200 Hz, producing full-body joint targets that S0 tracks at kilohertz rates. This “pixels-to-whole-body” architecture allows S1 to reason about the complete state of the robot and environment as a single coupled system.

The palm cameras and tactile sensors are new hardware capabilities from Figure 03, and this is the first demonstration of neural network policies that depend on these modalities. The tactile sensors embedded in each fingertip can detect forces as small as three grams—sensitive enough to feel a paperclip—enabling contact-aware, force-modulated grasping.

These sensing modalities unlock the full dexterity potential of five-fingered hands, tackling intricate manipulation tasks that demand the fine motor control of multi-fingered grasping.

A Three‑Tiered Control Architecture

The power behind Helix 02 lies in its hierarchical, three‑tiered architecture, tuned to different timescales while separating low‑level stability, mid‑level motor control, and high‑level goal decomposition.

At the foundation is System 0 (S0), a learned balance and coordination prior. Trained on large volumes of human motion data plus extensive simulation, S0 encodes what stable, natural human‑like motion looks like across a range of poses, transitions, and terrains. Instead of relying on tens of thousands of lines of hand‑coded balance logic, S0 learns a latent preference for joint configurations and center‑of‑mass trajectories that keep the robot upright and fluid.

Above that runs System 1 (S1), the visuomotor policy. S1 ingests head‑mounted cameras, palm‑mounted cameras, fingertip tactile sensors, and full‑body proprioception, then outputs high‑dimensional joint or end‑effector targets. This is the layer that actually closes the loop between perception and action: when Helix nudges the dishwasher with its foot or catches a slip in its grip, S1 is the engine decoding those sensorimotor streams into specific motor decisions.

At the top sits System 2 (S2), the semantic reasoning and task‑planning layer. S2 is where language, goal specifications, and scene understanding are translated into a stream of high‑level latent objectives — “walk to the dishwasher,” “unload dishes,” “re‑grasp the fallen plate,” “close the door.” It does not write individual motor commands; instead, it steers S1 by shaping what kind of behavior should be produced in context.

Together, the stack forms a pipeline from pixels to torque: from raw sensory input, through meaning and intent, down to coordinated, embodied motion across the whole body.

Long‑Horizon Autonomy in Real Kitchens

The dishwasher demo is less about flash and more about long‑horizon behavior in a real‑world setting.

Over a continuous multi‑minute sequence, Helix 02 executes dozens of loco‑manipulation actions: walking with dishes in hand, navigating around obstacles, maintaining stable grasps while re‑balancing, and quietly correcting for small slips and misalignments. The foot‑nudge is just one tactical choice in that extended time horizon as the system trades off geometry, stability, and task progress.

This is the frontier of long‑horizon autonomy: moving from short, isolated skills toward complex, unfolding plans that span minutes and multiple constraints. Traditional robotics often had to choreograph those sequences by explicitly chaining skills together and handling edge cases by hand. Helix 02 instead rolls them out inside one adaptive policy that implicitly tracks state and corrects course as the scene evolves.

From a VLA lens, the “plan” here is not a symbolic script of discrete actions. It is a temporally extended rollout of a neural controller conditioned on visual input and high‑level objectives, constantly updating as new sensory information arrives.

What Touch and In‑Hand Vision Add

Helix 02’s hardware stack deepens the VLA story by adding tactile sensing and in‑hand vision. Palm‑mounted cameras give the robot an up‑close view of objects as they contact the environment, while fingertip tactile sensors provide feedback on contact locations, shear forces, and slippage.

These modalities allow the robot to see and feel what head‑mounted cameras miss:

* Objects partially occluded by shelves, containers, or other dishes

* Fine contact geometry during grasping and re‑grasping

* Deformable or fragile items under load

With that feedback, Helix can attempt tasks that have historically been brittle for autonomous systems:

* Unscrewing bottle caps without crushing or over‑torquing

* Extracting a single pill from a cluttered box

* Dispensing a precise volume from a syringe

* Picking up tiny metal pieces from a visually busy tray

In a VLA‑style architecture, these extra signals enrich the embedding of physical affordances. Instead of grounding language and vision into abstract rules alone, the model is grounding commands into sensorimotor chunks that reflect what actually happens at the points of contact.

Why It Matters

For decades, locomotion and manipulation matured as separate subfields. Walking without falling is hard. Grasping without breaking things is hard. Having a robot walk, carry something delicate, adapt to slightly different surfaces and layouts, and still complete a multi‑step task raises the difficulty dramatically.

Classical robotics answered that challenge with modules: one controller for walking, another for grasping, a planner on top to arbitrate. Helix 02 represents the opposite trend — an integrated, learned stack where motion, balance, and manipulation emerge from a single behavior prior rather than being welded together from separate subsystems.

That does not mean the modular approach disappears, especially in safety‑critical settings. But it does suggest a new unit of competence: not “a planner plus a controller plus perception,” but a learned visuomotor policy that internally manages its own decomposition of behavior across space and time.

The foot‑nudge at the dishwasher is small. The embodied, whole‑body, VLA‑style intelligence it hints at is anything but.

Update: Handling Glassware

Twenty-four hours after the Helix 02 announcement, Figure demonstrated the robot handling glassware autonomously—unloading glass items from a dishwasher and placing them on counters.

This came in direct response to a question I posted on Figure’s announcement asking whether Helix could handle glassware. The capability was likely already there, but the question prompted Figure to show it publicly.

Glass is one of the hardest benchmarks in household robotics because it compresses nearly every unsolved problem in manipulation into a single task. It’s simple for humans. For robots, it’s brutal.

Glass requires precise force modulation (too much pressure and it shatters), stable multi-point contact (awkward grasps lead to slips), and real-time tactile feedback to detect micro-slips before they cascade into drops. There’s no margin for error and no visual warning before failure.

That Helix 02 handles both plastic and glass with the same underlying policy demonstrates what the S0/S1/S2 architecture enables: material-agnostic manipulation grounded in tactile feedback rather than object-specific rules. The robot doesn’t need separate controllers for fragile versus durable items—it adapts grip force based on what it feels at the points of contact.

This is exactly what a unified visuomotor policy should enable, and glass is the proving ground. See the original video here. (The image below is not clickable.)

Additional Resources for Inquisitive Minds:

Figure AI. Introducing Helix 02: Full-Body Autonomy.

Helix: A Vision-Language-Action Model for Generalist Humanoid Control

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Building Generalist Humanoid Capabilities with NVIDIA Isaac GR00T N1.6 Using a Sim-to-Real Workflow

Tactile Sensing in Robotics. Yuan, Wenzhen, et al. “GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force.” Sensors, 2017. Explains why touch sensing is critical for delicate object handling like glassware.

Podcast Note:

Merve, et al. “Vision Language Models (Better, faster, stronger).” Hugging Face, 12 May 2025, https://huggingface.co/blog/vlms. 23

Bandaru, Rohit. “Foundation Models for Robotics: Vision-Language-Action (VLA).” Rohit Bandaru, 28 Sept. 2025, https://rohitbandaru.github.io/blog/Foundation-Models-for-Robotics-VLA/. 1, 3

“Humanoid Robot Report. Figure 02.” Humanoid Robot Guide, https://humanoid.guide/product/figure-02/. 4, 8

“Figure AI Passes $1B with Series C Funding Toward Humanoid Robot Development.” The Robot Report, https://www.therobotreport.com/figure-ai-raises-1b-in-series-c-funding-toward-humanoid-robot-development/. 4, 11

“Figure Announces Strategic Partnership with Brookfield.” Figure AI, https://www.figure.ai/news/figure-announces-strategic-partnership-with-brookfield. 4, 13

“F.02 Contributed to the Production of 30,000 Cars at BMW.” Figure AI, https://www.figure.ai/news/production-at-bmw. 4, 14

“Helix: A Vision-Language-Action Model for Generalist Humanoid Control.” Figure AI, 20 Feb. 2025, https://www.figure.ai/news/helix. 4, 15

“Humanoid Robot Market Report 2026.” Humanoid Robot Guide, https://humanoid.guide/humanoid-robot-market-report/. 1, 16

“Innovative Humanoid Robots in 2025–2026 - Reality or Hype?” Winssolutions, 28 Jan. 2026, https://www.winssolutions.org/humanoid-robots-2025-2026-reality-hype/. 1, 17

“Introducing Helix 02: Full-Body Autonomy.” Figure AI, https://www.figure.ai/news/helix-02. 4, 18

Kalil, Mike. “Figure AI and OpenAI Break Up.” Mike Kalil, https://mikekalil.com/blog/figure-openai-breakup/. 1, 19

Malobický, Branislav, et al. “Towards Seamless Human–Robot Interaction: Integrating Computer Vision for Tool Handover and Gesture-Based Control.” Applied Sciences, vol. 15, no. 7, 2025, p. 3575, https://doi.org/10.3390/app15073575. 1, 22

Orrall, Jesse. “AI Brains in a Humanoid Robot: Meet Figure 02.” CNET, 6 Aug. 2024, https://www.cnet.com/tech/computing/ai-brains-in-a-humanoid-robot-meet-figure-02/. 4, 24, 25

“Project Go-Big: Internet-Scale Humanoid Pretraining and Direct Human-to-Robot Transfer.” Figure AI, 18 Sept. 2025, https://www.figure.ai/news/project-go-big. 4, 26

Rebellionaire Staff. “Figure 03 vs Tesla Optimus: Our Real-Time Reaction.” Rebellionaire, 9 Oct. 2025, https://www.rebellionaire.com/post/figure-03-vs-tesla-optimus-reaction. 4, 27

Ridden, Paul. “Video: GPT-Enhanced Humanoid Speaks and Reasons as It Works.” New Atlas, 13 Mar. 2024, https://newatlas.com/robotics/figure-01-openai-humanoid-robot-real-time-conversations/. 4, 28

Sapkota, Ranjan, et al. “Vision-Language-Action Models: Concepts, Progress, Applications and Challenges.” arXiv, 2025, https://arxiv.org/html/2505.04769v1. 4, 29

Sarraf, Gaurav. “Tesla Robot Price vs Competitors: Which Humanoid Robot Offers Best Value?” ThinkRobotics, 26 Dec. 2025, https://thinkrobotics.com/blogs/learn/tesla-robot-price-vs-competitors-which-humanoid-robot-offers-best-value. 1, 30

Sharma, Nitika. “Figure’s Helix: AI that Brings Human-Like Robots to your Home.” Analytics Vidhya, 21 Feb. 2025, https://www.analyticsvidhya.com/blog/2025/02/figures-helix/. 4, 31

“Tesla Optimus: Complete Analysis of AI, Specs & Future Outlook (2026).” Botinfo.ai, 27 Jan. 2026, https://botinfo.ai/articles/tesla-optimus. 1, 32

“Top 12 Humanoid Robots of 2026.” Humanoid Robotics Technology, https://humanoidroboticstechnology.com/articles/top-12-humanoid-robots-of-2026/. 1, 33

“Vision-Language-Action Model.” Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Vision-language-action_model. 4, 34

#Helix02 #EmbodiedAI

As NVIDIA summed up the problem: AVs must safely operate across an enormous range of driving conditions. Rare, complex scenarios, often called the “long tail,” remain some of the toughest challenges for autonomous systems to safely master. Traditional AV architectures separate perception and planning, which can limit scalability when new or unusual situations arise.

The Alpamayo Stack: Think Before You Steer

Named after a Peruvian peak, Alpamayo is NVIDIA’s new “reasoning AI” platform for cars: a portfolio of ~10-billion-parameter models that take in real-time video and sensor data, break it into decision sub-tasks, and generate a trajectory—all while explaining their reasoning in natural language.

Think ChatGPT, but for merging onto I-405. And then doing it again. And again. Safely. Verbally. Auditable. That’s Alpamayo’s hook: explainability.

It’s not just a model, either. It’s a stack:

* AlpaSim: High-fidelity closed-loop simulation that trains and tests AI agents in virtual cities.

* Halos: A backup “classical” safety stack that monitors the AI’s choices and can yank the wheel if needed.

* Open datasets: Released publicly, hosted on Hugging Face. Yes, the weights are coming too.

Mercedes Is First, But Not Last

The first commercial deployment is the 2026 Mercedes-Benz CLA, built on the MB.OS architecture and NVIDIA’s full DRIVE AV stack. What you get: a Level 2++ system that handles point-to-point driving in urban and highway settings—hands on, eyes up, but AI in the loop the whole way.

Behind the scenes, that stack combines Alpamayo’s neural-policy brain with classic safety logic. It watches, reasons, explains. And if it screws up, the backup stack steps in.

That car will be on U.S. roads in 2026. Europe follows. Asia comes in 2027. No overpromising. Just a slow, deliberate ramp with regulators and human drivers fully in the mix.

“The ChatGPT moment for physical AI is here — when machines begin to understand, reason and act in the real world,” said Jensen Huang, founder and CEO of NVIDIA. “Robotaxis are among the first to benefit. Alpamayo brings reasoning to autonomous vehicles, allowing them to think through rare scenarios, drive safely in complex environments and explain their driving decisions — it’s the foundation for safe, scalable autonomy.”

Open vs. Closed: Tesla, Meet Your Challenger

Unlike Tesla’s fully vertical Full Self-Driving, Alpamayo is horizontal. It’s meant to be adopted. Remixed. Forked. Regulators can step inside the loop. Tier 1s can build on top. Startups can train derivatives. It’s AI driving as an ecosystem—not a black box.

It’s also NVIDIA’s attempt to commoditize supervised autonomy. If every OEM can license a competitive Level 2++ experience, Tesla loses its moat. If Alpamayo proves safer, more transparent, or cheaper to scale, it might not matter who started first.

Even Elon’s noticing. He told investors he’s “not losing sleep.” The rest of the market? Maybe it should.

Rubin Powers the Brain

Don’t forget the muscle. Alongside Alpamayo, NVIDIA dropped Rubin: a hyperscale AI compute platform for training physical-world models—autonomy, robotics, even humanoids. (Yes, there was a robot teaser too.)

OEMs who don’t want to build a GPU farm can rent one instead. Which means even a mid-tier automaker can punch above its weight—training L4 stacks, testing new policies, iterating in simulation, and deploying with Alpamayo as the core.

The Takeaway: Autonomy as Infrastructure

Mercedes is the first to ship it, but anyone can plug in. That’s the play. NVIDIA isn’t building the car. It is building the brain. Licenseable. Inspectable. Adaptable.

The closed-stack era is ending. The open-stack war just began.

Tesla has FSD. Waymo has the Driver. NVIDIA is offering something else.

Not a car. Not a demo. A thinking, explainable autonomy stack built for the industry.

That changes the equation. The autonomy stack is no longer a secret weapon. It is a product category.

Additional Reading for Inquisitive Minds:

NVIDIA Newsroom. NVIDIA Announces Alpamayo Family of Open-Source AI Models and Tools to Accelerate Safe, Reasoning-Based Autonomous Vehicle Development. January 5, 2006.

Waymo Driver. Accessed January 6, 2026.. “We’re building a Driver that’s leading the way for the entire industry. Informed by unmatched experience and designed with safety at its heart, the Waymo Driver is our autonomous driving technology that never gets drunk, tired, or distracted.”

NVIDIA Blog. Alpamayo, a Foundation Model for Driving. CES 2026 Keynote Coverage.

Electrek. NVIDIA Unveils Open-Source AI for Autonomous Driving, Ships in Mercedes-Benz CLA in Q1 2026. January 5, 2026.

The Verge. Everything NVIDIA Announced at CES 2026. January 5, 2026.

Engadget. NVIDIA Launches Alpamayo AI Stack for Open Autonomous Driving. January 5, 2026.

Reuters. Mercedes to Offer NVIDIA Autonomous Driving Tech on U.S. Streets in 2026. January 5, 2026.

GovInfoSecurity. NVIDIA Bets on Reasoning AI for Self-Driving Cars. January 6, 2026.

Seeking Alpha. NVIDIA Reveals Alpamayo: A Thinking Model for AVs. January 5, 2026.

The attached podcast was created using Google’s NotebookLM technology. Here are the sources I used to create the “notebook.” Alpamayo for Autonomous Vehicle Development.” NVIDIA Developer, 2026.

Caulfield, Brian. “NVIDIA Rubin Platform, Open Models, Autonomous Driving: NVIDIA Presents Blueprint for the Future at CES.” NVIDIA Blog, 5 Jan. 2026.

Furrier, John. “Nvidia at CES: Alpamayo signals the real arrival of physical AI.” SiliconANGLE, 5 Jan. 2026.

Lambert, Fred. “Nvidia unveils open-source AI for autonomous driving, ships in Mercedes-Benz CLA in Q1 2026.” Electrek, 5 Jan. 2026.

Marian, Sorca. “NVIDIA just launched Alpamayo, a ‘reasoning’ autonomous driving AI model - and it changes the Tesla vs Waymo narrative.” abZ Global, 6 Jan. 2026.

“NVIDIA Announces Alpamayo Family of Open-Source AI Models and Tools to Accelerate Safe, Reasoning-Based Autonomous Vehicle Development.” NVIDIA Newsroom, 5 Jan. 2026.

“Nvidia CES 2026: Alpamayo Open-Source AI Marks Physical AI Era.” Stock Titan, 5 Jan. 2026.

Pavone, Marco. “Building Autonomous Vehicles That Reason with NVIDIA Alpamayo.” NVIDIA Technical Blog, 5 Jan. 2026.

Pavone, Marco, et al. “Alpamayo 1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail.” NVIDIA Research, 28 Oct. 2025.

Recoil42. “Nvidia launches Alpamayo, open AI models that allow autonomous vehicles to ‘think like a human’.” Reddit, r/SelfDrivingCars, Jan. 2026.

#ces2026 #NVIDIA #alpamayo #physicalAI

On Monday at the Consumer Electronics Show in Las Vegas, Boston Dynamics and its majority owner, Hyundai Motor Group, said that Atlas is now being prepared for real industrial use. Hyundai plans to begin deploying the humanoid robot in its factories starting in 2028, marking a shift from research demonstrations to commercial operation.

The announcement places Boston Dynamics among a growing group of companies attempting to introduce humanoid robots into workplaces, where expectations for reliability and safety are significantly higher than in laboratory settings.

From Research Platform to Product

Boston Dynamics was founded in 1992 as a spinout from the Massachusetts Institute of Technology and spent much of its early history focused on government-funded research into robotic mobility. The company changed hands several times before Hyundai acquired an 80 percent controlling stake in 2021.

Since then, Boston Dynamics has increasingly emphasized commercialization. It already sells Spot, a four-legged inspection robot, and Stretch, a mobile robot designed for warehouse unloading. Atlas, however, remained a research platform until now.

At CES, the company confirmed that the current version of Atlas is considered a commercial product. This version is fully electric, replacing earlier hydraulic systems, and is designed for repeatable manufacturing and long-term use in industrial environments.

Factory Deployment Beginning in 2028

Hyundai said it plans to introduce Atlas into its manufacturing operations beginning in 2028, starting with facilities in the United States. The first deployments are expected at the company’s electric vehicle manufacturing complex in Savannah, Georgia.

Initial tasks will focus on parts sequencing and material handling, which involve moving and organizing components for assembly lines. These roles are physically demanding and repetitive, making them a common target for automation.

Hyundai executives emphasized that Atlas is intended to work alongside human employees rather than replace them outright, at least in its early stages.

Technical Capabilities

Boston Dynamics has disclosed limited technical specifications, but the company has said Atlas has approximately 56 degrees of freedom, allowing for complex movements of the arms, legs, and torso. The robot is equipped with advanced sensors in its hands to detect contact and force, enabling more precise manipulation of objects.

Atlas is designed to lift loads of roughly 50 kilograms, or about 110 pounds, and to operate continuously using automated battery replacement. These capabilities are aimed at meeting the physical demands of factory work.

Unlike earlier versions that relied heavily on preprogrammed motions, the commercial Atlas is intended to use more autonomous perception and decision-making, an area that has historically limited humanoid robots.

Artificial Intelligence and Simulation

To address those limitations, Boston Dynamics is expanding its use of artificial intelligence and simulation tools. One key partnership highlighted at CES is with NVIDIA, whose Omniverse platform allows robots to be trained in simulated environments before being deployed in the real world.

Simulation enables engineers to expose robots to a wide range of scenarios without the cost or risk of physical testing. This approach is increasingly seen as essential for deploying robots safely at scale.

Boston Dynamics has also disclosed collaboration with Google DeepMind, which is developing large AI models designed for robotics. These models aim to improve a robot’s ability to interpret its surroundings and adapt to new tasks.

Why Manufacturing Comes First

The decision to deploy Atlas in factories rather than homes reflects practical considerations. Industrial environments are structured and predictable, with standardized tasks and layouts. That makes them more suitable for early humanoid deployments than domestic settings.

Manufacturers also face ongoing labor shortages, particularly for physically demanding roles. By using Atlas internally, Hyundai can refine the robot’s performance while addressing its own operational needs.

Hyundai has indicated long-term ambitions to produce humanoid robots at scale, potentially in the tens of thousands per year, though it has not provided detailed production timelines.

A Cautious Step Forward

Humanoid robots have been promised before, often with timelines that slipped or failed to materialize. What distinguishes this announcement is the combination of a specific deployment year, a defined use case, and a major industrial customer that owns the robot manufacturer.

Whether Atlas meets expectations will depend on factors that are difficult to demonstrate on a stage, including durability, cost, and the ability to operate safely over long periods. Boston Dynamics has not disclosed pricing or performance targets.

Still, the shift from demonstration to planned deployment marks a notable moment for the field. After years as a research icon, Atlas is being prepared for routine work.

Additional Reading for Inquisitive Minds:

Reuters. “Hyundai Motor Group plans to deploy humanoid robots at U.S. factory from 2028.” Jan. 5, 2025.

TechRadar. “Boston Dynamics’ Atlas humanoid robot is now a product and heading to factories.” Jan. 5, 2025.

Associated Press. “Hyundai and Boston Dynamics unveil humanoid robot Atlas at CES.” Jan. 5, 2025.

Hyundai Motor Group. Newsroom. “Hyundai announces AI robotics strategy at CES.” Jan. 2025.

Automation World. “Boston Dynamics and NVIDIA collaborate on next-generation AI capabilities for humanoid robots.” Jan. 2025.

Podcast created using Google’s NotebookLM technology. Sources for the podcast: “AI News.” “Boston Dynamics Reveals 2026 Atlas Endgame (AI NEWS).” YouTube, uploaded by AI News, [Transcript].

Arthur K. “Boston Dynamics Is Training Atlas to Handle Real Factory Tasks With AI.” Ghacks.net, 5 Jan. 2026.

“Atlas.” Boston Dynamics, Product Page.

“Boston Dynamics & Google DeepMind Form New AI Partnership to Bring Foundational Intelligence to Humanoid Robots.” Boston Dynamics, 5 Jan. 2026.

“CES 2026: Hyundai Motor Group leverages Group capabilities to lead the AI Robotics industry.” Hyundai Worldwide, 5 Jan. 2026.

Choi, Song-a. “Hyundai Motor Wins Its First CES Best Innovation Award, Proving Its Edge in Robotics.” KMJ, 5 Jan. 2026.

Chung, Gina. “The Hidden ROI of RaaS: Robots That Deliver Increasing Value Over Time.” Locus Robotics, 22 Oct. 2025.

ContemplativeNeil. “Forget Tesla .. Remember Atlas? (Boston Dynamics).” Reddit, r/robotics, 2026.

“Enterprise Robotics, Redefined.” Boston Dynamics, 5 Jan. 2026.

“Hyundai Motor Group Announces AI Robotics Strategy to Lead Human-Centered Robotics Era at CES 2026.” Hyundai Worldwide, 5 Jan. 2026.

“Hyundai Motor Honored with CES 2026 Best of Innovation Award in Robotics for MobED Droid.” Hyundai Worldwide, 4 Jan. 2026.

Kalil, Mike. “Tesla Optimus vs. Figure 03: Humanoid Robots Show Off Running Abilities.” Mike Kalil, 5 Dec. 2025.

Kan, Michael. “Boston Dynamics’ New Atlas Robot Is Here: Will It Replace Your Job?” PCMag, 17 Apr. 2024.

Lee, Min-hyung. “Hyundai Motor’s autonomous mobility robot wins innovation award at CES.” The Korea Times, 5 Jan. 2026.

Lopez, Jose Antonio. “Partnering Humans and Co-Working Robots: Boston Dynamics Atlas Ushers in the Era of Human-Centered Smart Factories.” The Korean Car Blog, 5 Jan. 2026.

Pan, Yichao, et al. “Advancing Robot Learning, Perception, and Manipulation with Latest NVIDIA Isaac Release.” NVIDIA Technical Blog, 6 Jan. 2025.

Park, Sohyun. “Hyundai Motor Company’s MobED Wins ‘Best of Innovation Award’ in Robotics at CES 2026.” Maeil Business News Korea, 5 Jan. 2026.

Staff. “How Much Does Warehouse Automation Cost? Is RaaS an Option?” Vecna Robotics, 14 Mar. 2023, updated 15 Jul. 2024.

“The Industrialization of Humanoid Robotics: A Strategic Evaluation of Boston Dynamics’ Atlas Commercial Debut at CES 2026.” [Source Text], 5 Jan. 2026.

The Tech Buzz. “Boston Dynamics Partners With Nvidia, Google on Consumer Robots at CES.” The Tech Buzz, 5 Jan. 2026.

Ulanoff, Lance. “Boston Dynamics’ Atlas humanoid robot is now a product and heading to factories in 2028.” TechRadar, 5 Jan. 2026.

Wadowsky, Lauren. “CES 2026: Boston Dynamics Atlas humanoid robot has an AI brain.” Gadget Flow, 5 Jan. 2026.

Zuckerman, Gregory. “Atlas, the Boston Dynamics DeepMind Robot.” FindArticles, 5 Jan. 2026.

#humanoids #atlas #bostondynamics

The company is Psyonic. The founder is Aadeel Akhtar. What he was wearing was a costume, but it functioned as a wearable demonstration of a serious idea about how robots will eventually learn to use their hands.

Akhtar calls it his Doc Ock suit, and it is an outstanding replica of the Marvel character. But, even if you don’t know anything about Doc Ock, you’d be hard-pressed not to be drawn in by the suit. Four robotic hands moved with intent, controlled by his own hands through gloves. Right hand controlling the right side. Left hand controlling the left. The setup was intuitive enough that you understood it before it was explained.

That is when the conversations started.

Psyonic builds advanced bionic hands that are used by both humans and robots. This is not a theoretical claim. Nearly 300 patients use the Ability Hand as a prosthetic, covered by insurance and Medicare. At the same time, more than 50 robotics companies use the exact same hand on robots, including NASA, Meta, Google, Amazon, and automotive manufacturers working through humanoid platforms.

That overlap is the point.

When I asked Akhtar where he sees the industry going, he did not talk about better motors or more parameters. He said the hand problem is a data problem. Robots struggle with dexterous manipulation not because we do not know how to build hands, but because we do not have the right training data. Especially when it comes to soft, deformable objects that require constant force adjustment.

A human knows how to pick up a fragile object without crushing it. Not by calculation, but by feel.

What makes Psyonic different is that the same hand goes on a human and on a robot. Human users are already doing the tasks robots are trying to learn. Picking and placing. Sorting. Folding laundry. Cooking. Working in industrial environments. Because the hardware is identical, the data transfers cleanly. This is real to real learning without translation loss.

That distinction matters more than it sounds.

Most robotics demos show hands mounted to tables, performing narrow tasks under ideal conditions. Psyonic’s hands live on real bodies. They experience friction, hesitation, mistakes, and recovery. They generate data shaped by the messiness of the real world. That data is what robots actually need.

In 2024, Psyonic won a rare three-shark deal on Shark Tank. It was a brief moment in the spotlight for a company that spends most of its time solving hard problems far from TV cameras.

Seeing a PYSONIC limb in action is very impressive when it is worn by a patient making use of the technology in their everyday lives.

Here’s the video that goes with this image:

Akhtar has spent his entire career at the intersection of neuroscience, electrical engineering, robotics, and medicine. His PhD focused on closed loop sensorimotor control for upper limb prosthetics. Psyonic is not a pivot or a trend response. It is the natural outcome of someone who has been thinking about how humans and machines connect for more than a decade.

When I asked how far off this future is, he didn’t overpromise. He said within the next five years. Calmly. Confidently. Like someone who has shipped hardware before.

Walking away from that interaction, what stayed with me wasn’t the spectacle. It was the clarity. Hands are the bottleneck. Mobility is improving quickly. Intelligence is scaling. But manipulation remains hard because touch is hard.

Psyonic is quietly building the missing bridge by starting where touch already works.

With humans.

That choice has consequences beyond robotics. In a field where advanced prosthetic systems can cost well into six figures, Psyonic has focused on compressing that gap, making high-performance bionics dramatically more accessible to the people who rely on them every day. This isn’t a side effect of their work. It’s the foundation.

Bionics, in this framing, aren’t just assistive devices or flashy demos. They’re a training ground. A way to turn lived human experience into real-world capability for machines.

This is what the next phase of robotics looks like. Less magic. More embodiment. Less simulation. More reality.

Not robots pretending to be human.

But robots finally learning from us.

Find out more about the company: PSYONICFind out more about the founder., Aadeel Akhtar, PhD.#robotics

Then he started talking.

Bulatov is thoughtful, calm, and genuinely kind. Not the personality you expect behind a robot fighting league. Which turns out to be the point.

On the surface, UFB looks exactly like what it sounds like. Humanoid robots in a cage, throwing punches, falling down, getting back up. It is loud. It is physical. It is undeniably entertaining. But fighting is not the core idea. It is the delivery mechanism.

What UFB is really building is a league built around human and robot teams. A person pilots a real humanoid robot remotely and competes against another human-robot pair. The robot is not autonomous by default, and that choice is deliberate.

As Bulatov explained, people do not actually want to watch perfect machines execute flawless motions. They want story. They want connection. They want to follow a robot and a human learning how to work together under pressure. The competition is real, but so is the partnership.

Fighting, in this context, is a forcing function. It creates edge cases on demand. Balance failures. Awkward impacts. Missed timing. Recovery moments. The kinds of physical situations humanoids struggle with most, compressed into a few minutes. That makes it an unusually effective stress test for embodied AI.

It also makes the experience accessible.

One of the most surprising parts of the conversation was how easy it is to start piloting a robot. You do not need a VR rig or specialized hardware. If you can use a game controller, you can control a fighting robot. This is not about elite operators. It is about lowering the barrier so more people can participate.

Bulatov talked about wanting people to leave excited enough to try robotics themselves. To control a robot. To program one. To imagine building a team. Entertainment is the entry point, not the end goal.

He also walked me through how anyone can try this right now through the UFB remote gym. I did. Somewhere in Beijing, a robot named Sam fell over because of choices I made from my laptop.

This was not what I expected.

The surprising part was not the fact that the robot fell. It was how direct the connection felt. Press a button here, a humanoid moves there. Press another, it loses balance and goes down. It makes the human role in the loop very real, very fast.

Looking ahead, UFB is aiming for something closer to Formula One than a one-off spectacle. Teams with their own robots. Their own researchers. Their own pilots and repair crews. A league where success depends on hardware, software, human skill, and fast fixes between rounds.

Bulatov is convinced robot fighting will become one of the defining sports of this century. That sounds bold until you realize what he is really describing. Not violence, but participation. Robots that people can control, understand, and emotionally invest in.

UFB uses fighting to make humanoids legible. To make failure visible. To make learning public.

The punches are just how they get your attention.#HumanoidRobotics

I was standing at the Lightwheel booth, holding a pair of game controllers, operating a robotic arm. The task sounded trivial: grab the connector, pull, unplug.

I failed. Repeatedly. I don’t think I even came close.

As a human, unplugging a cable is something you do without thinking. You grab it, wiggle a little, pull — done. For a robot, every part of that interaction has to be learned: where to grab, how hard to pull, what to do when the connector resists, and how to recover when things don’t line up perfectly.

The two-minute video above shows exactly what that learning process looks like. It’s awkward. It’s slow. And it’s far harder than it appears.

That small, frustrating demo explains more about the state of robotics today than any polished keynote or glossy humanoid reveal. Training robots isn’t just about building better hardware or bigger models. It’s about teaching machines how to deal with the messy physical details humans take for granted.

Why Simple Tasks Break Robots (and apparently, Diana, too.)

In the race to build general-purpose robots — from autonomous vehicles to humanoids — hardware keeps improving. Motors get stronger. Sensors get cheaper. Form factors get sleeker.

Software, however, is starving.

Physical AI systems need enormous amounts of experience to behave reliably in the real world. But gathering that experience physically is slow, expensive, and risky. You can’t crash cars endlessly to see what happens. You can’t let humanoid robots repeatedly fail in kitchens, warehouses, or factories.

This is why simulation has quietly become essential infrastructure for robotics.

Simulation Is Harder Than It Sounds

Simulation sounds straightforward in theory. Build a virtual world. Drop a robot into it. Let it practice.

In reality, it’s brutally difficult.

First, there’s asset discovery. Engineers spend huge amounts of time just finding the right objects to populate a simulation, not “a cable,” but this cable, with the right shape, stiffness, and friction.

Second, there’s physics fidelity. Most simulations assume a rigid world. But the real world is full of non-rigid, deformable objects: cables, cloth, food, wires, plants. These are exactly the things that cause robots to fail once they leave the lab.

And third, there’s evaluation. A robot can succeed endlessly in simulation and still fail the moment it touches reality. Simulation is a proxy, not the real thing, and without careful validation it can create false confidence.

Practicing against a tennis ball machine helps, but it won’t prepare you for wind, pressure, or a slippery court. At some point, you have to play the match.

Lightwheel: Building Worlds for Robots to Learn In

Lightwheel isn’t building robots. They’re building the worlds robots learn in.

Founded by Steve Xie, formerly a lead on autonomous driving simulation at NVIDIA and Cruise, Lightwheel focuses on reducing friction in simulation workflows rather than promising to “solve” sim-to-real outright.

I had the opportunity to interview members of the Lightwheel team. What stood out was how they frame simulation not as a visualization tool, but as a behavioral test environment.

Their framework separates two layers:

* a behavior layer, which captures what a robot does

* a world layer, which represents where it does it

The goal isn’t a perfect digital copy of reality. It’s a world realistic enough to stress-test behavior and measure how well it generalizes.

Rather than treating sim-to-real as a binary success or failure, they treat it as a gradient. How far does a learned behavior transfer? Where does it break? How bad is the failure when it happens?

Robots struggle most with everyday, squishy, flexible stuff, because those things don’t behave the same way twice.

Those are exactly the objects that make robots fail once they leave the lab.

Who This Matters For — Right Now

This approach is especially relevant for:

* Academic labs, which need reproducible environments and standardized assets

* Humanoid and embodied AI teams, facing a massive data gap

* Industrial teams, working in factories full of cables, cloth, fluids, and flexible materials

For startups, this kind of infrastructure avoids building simulation tooling from scratch. For larger organizations already using platforms like NVIDIA Isaac Sim, it augments existing workflows instead of replacing them.

Autonomous Driving Has Been Here Before

If this all sounds familiar, it should.

Autonomous driving has been wrestling with these problems for years. Systems can feel smooth and confident one moment, then unsettling the next, not because they’re broken, but because they run into situations they haven’t experienced deeply enough.

Autonomy rarely fails loudly. It fails quietly, in ways that erode trust.

My husband recently tried Tesla’s Full Self-Driving, accidentally switched on Mad Max Mode, and decided he was done with it.

He expected a quiet ride. He did not get one. The system wasn’t broken. It just felt wrong.

Why This Moment Matters

The real world is messy in very specific ways. Objects bend, resist, and change based on how they’re handled, and those small variations are exactly what make training robots so difficult.

Trust isn’t trained by benchmarks alone. It’s trained through exposure to awkward, frustrating, real-world moments we usually ignore.

As robots move out of labs and into human spaces, simulation is no longer optional. It’s infrastructure. And the quality of the worlds we build for robots to practice in may matter as much as any single breakthrough model.

Training robots is hard.

That’s the point.

Editor’s Note: The accompanying podcast was created using NotebookLM. Notebook sources.

Sources:

“Beyond Rigid Worlds: Representing and Interacting with Non-Rigid Objects Workshop.” CoRL 2025, 27 Sept. 2025, Seoul, Korea. Conference Workshop Program.

Demaitre, Eugene. “NVIDIA Releases Cloud-to-Robot Computing Platforms for Physical AI, Humanoid Development.” The Robot Report, WTWH Media, 19 May 2025.

“End-to-End Autonomous Driving Industry Report, 2024-2025.” ResearchInChina, Dec. 2024.

“Lightwheel-Platform Enterprise.” Lightwheel, Lightwheel Inc., 2025.

Lim, Sudo. “Deals in Brief: 81Ravens Raises Funding, Honor Secures Investment Ahead of IPO, Five Other China Deals, and More.” KrASIA, 1 Nov. 2024.

Mustafa. “Creating a Simulation Environment for Robot Training Is Hard, but Accelerating Asset Discovery Using USD Search Makes It Easier.” Lightwheel, 29 Sept. 2025.

“We’re starting with simple tasks to bootstrap our foundational model.”– Bren Pierce, CEO and Founder of Kinisi Robotics

In an era where humanoid robots are drawing headlines for dancing, sprinting, or flipping switches with eerily human hands, Kinisi Robotics is charting a different course — one grounded not in spectacle, but in reliability.

Their flagship robot, KR‑1, isn’t designed to wow audiences. It’s built to show up, lift crates, and do the work — safely, efficiently, and without a marketing team on standby.

“Our KR‑1 humanoid robot is designed for the warehouse and factory space,” says CEO Bren Pierce, who believes the future of robotics is less about human mimicry and more about human utility.

A Humanoid That Moves with Purpose

KR‑1 walks the line — metaphorically — between form and function. Its upper body takes design cues from humans, making it naturally suited to environments built for people. But instead of legs, KR‑1 moves on a wheeled base — a choice driven by engineering pragmatism.

“Warehouses are flat,” Pierce points out. “We don’t need 14 motors if two will do.”

That simple equation shapes the entire robot. KR‑1 is engineered for tasks like lifting and delivering crates, picking objects, and navigating complex indoor spaces — not to imitate human movement, but to solve real problems quickly and affordably.

Field-Tested, Factory-Tough

Where many robots are still making the leap from lab to warehouse, KR‑1 is already being tested across pilot facilities in Europe and North America. These trials are more than checklists — they’re stress tests, designed to challenge the robot’s autonomy, perception, and endurance in messy, real-world environments.

This field data is central to Kinisi’s iterative design process. Instead of engineering in a vacuum, the company is learning from operators on the ground — and adapting quickly.

“Robots are in pilot facilities in Europe and America,” Pierce confirms. “We’re learning what’s needed, not just what’s possible.”

Safe by Design

Working alongside humans comes with responsibilities, and KR‑1 takes that seriously. The robot uses advanced sensors, full-environment perception, and soft-force control systems to ensure smooth, predictable behavior around people.

“It can see people and will slow down and stop when they get close.”

Full safety certification is underway, with initial deployments in semi-separated zones. The vision is clear: human-robot collaboration without compromise.

And unlike cages or warning zones, KR‑1’s behavior is built to be fluid and situational, adapting in real time to the pace and flow of human workers.

Start Small, Scale Smart

KR‑1 begins with a focused skillset — lifting, moving, delivering. But it’s designed to grow. Kinisi’s strategy is to earn trust through reliability, then expand the robot’s capabilities as operators discover new use cases.

That “bootstrapping” approach mirrors how many of today’s biggest tech platforms started: do one job extremely well, then build from there.

“We’re starting with simple tasks to bootstrap our foundational model.”

This is robotics not as a spectacle, but as infrastructure — steady, quiet, and transformative over time.

In Good Company, With a Different Approach

Kinisi doesn’t see itself as racing against other humanoid pioneers — but rather contributing a new piece to the puzzle.

While companies like Agility Robotics, Figure AI, and Tesla explore bipedal mobility and expressive motion, Kinisi is doubling down on operational dependability, minimal complexity, and deployment readiness. It’s not a question of right or wrong — just a difference in what’s needed first.

If we want robots to become part of the workforce, they need to show up ready to do the job. KR‑1 was built to do exactly that.

Vocabulary Key

Mobile manipulator: A robot that combines a moving base with robotic arms, able to navigate and interact with its environment.

360-degree perception: Sensor fusion that gives the robot awareness of its surroundings in all directions.

Human-safe design: Engineering principles that prevent robots from harming or startling nearby workers.

Pilot deployment: A real-world test run, often used to refine robot behavior before full rollout.

Bootstrapping model: A strategy that starts with simple, reliable tasks and scales capabilities gradually.

FAQs

Is KR‑1 a fully humanoid robot? It’s humanoid in function, not form. The robot has arms and a head-like sensor suite but uses wheels instead of legs for efficiency.

What can it do today? Lift crates, pick and place items, deliver payloads across a facility, and avoid obstacles and people in real time.

How is it trained or programmed? Operators can train KR‑1 through demonstration, reducing the need for specialized code or robotic expertise.

Is it safe to work near? Yes. KR‑1 uses real-time human detection and soft-stop behavior. Safety certification is expected within the next year.

Where is it being used? Pilot programs are active in Europe and the U.S., primarily in warehouses and light manufacturing environments.

Further Reading

Kinisi Robotics official site

NextTech Today. Kinisi Robotics Focuses on Wheeled Humanoids for Industry

5 Questions with Kinisi CEO Brennan Pierce – The Robot Report

Agility Robotics and the Digit roadmap

Humanoids in 2025: Hype or hardware? – Humanoid Guide

#HumanoidRobotics #WarehouseAutomation #IndustrialRobotics #LogisticsTech #RoboticsInnovation #MobileManipulators #RobotDeployment #FactoryAutomation #RobotDesign #PhysicalAI

“Start Self-Driving,” the Tesla urged with a prominent blue-grey button in the corner of the screen.

“We should give it a go,” I encouraged as we headed home from the gym.

“I’m not a fan of FSD,” my husband, who was driving, responded.

“Wait until we’re on a straightaway,” I urged. “What harm could it do then?”

But, once we reached a straight stretch of road, he tapped the button. A test drive of the future.

First thing the car did? It lunged for the shoulder.

I hadn’t even gotten the camera on yet. He swerved hard to override it.

Welcome to Tesla FSD.

The Fastest Autonomy on the Market

FSD v14.2.1 is Tesla’s latest attempt to bring true autonomous driving to consumer vehicles. It runs end-to-end on a vision-only neural net. No lidar. No radar. Just cameras and deep learning.

And unlike Waymo or Cruise, Tesla doesn’t fence its self-driving into safe zones or call centers full of remote operators. If you have the hardware (HW4), a steering wheel, and enough courage? You’re in.

According to Elon Musk, FSD v14.2 is finally ready for “wide release.” He’s called it Tesla’s best software yet — and hinted that v14.3 could be the leap that makes unsupervised driving real. The plan? Get rid of human drivers entirely by 2026.

But right now, as our hands hovered near the wheel, one thing was clear:

This car still needs a babysitter.

FSD in the Wild: What It Got Right

To its credit, FSD did a lot well:

* Green light handling: No hesitation. It read signals clearly and moved through smoothly.

* Flashing red: It interpreted the signal as a stop sign (correct) and executed a proper stop.

* Speed match: When behind another car doing 65 in a 50, it accelerated to match. Aggressive? Sure. But natural.

* Traffic signal warnings: On Monterey Highway, it correctly ignored a blinking red paired with a “signal ahead” sign. Old versions might have phantom-stopped.

These were clear improvements over FSD on HW3 (which I also drive). On that system, traffic lights felt like roulette. Here, they felt normal.

That’s no small leap.

...And What Still Feels Weird

The system also:

* Changed lanes without asking. We hadn’t entered a destination, but it picked routes anyway. Turns out FSD will sometimes act on latent nav preferences unless explicitly turned off.

* Refused to let us adjust speed upward. Chill mode limited acceleration. We tried scroll wheels (nothing), accelerator (worked, but temporarily disabled braking), and eventually discovered we could slow it down with the scroll wheel — not speed it up.

* Creeped back to slower speeds. Even after we added speed, it slowly dropped back into granny mode.

* Accelerated to 69 in a 50 approaching town. We did not tell it to do that.

“It’s like a very smart Autopilot,” my husband said. “As long as you’re going straight.”

Are the Cars...Aware?

After one sudden lane change, I told my husband:

“It’s just an algorithm. It’s doing what it was told.”

That’s technically true — but Tesla sometimes talks like it’s more than that. Elon Musk has claimed FSD cars are becoming “sentient.” Others call them “aware.”

Reality? These are powerful probabilistic systems making real-time decisions based on enormous datasets and inference models. They mimic awareness. But they’re not thinking. They’re reacting — fast.

And sometimes, a bird poops on the camera and the whole system says:

“Front camera obstructed.”

(Which might explain the shoulder dive. But the system didn’t catch it until about 25 minutes later.)

A Tale of Two Autonomies

Tesla’s approach (camera-only, fast iteration, consumer beta testing) puts it at odds with the rest of the auto industry. Most automakers are siding with Waymo-style autonomy:

* Redundant sensors (lidar + radar + cameras)

* Defined operational zones

* External validation & regulatory alignment

Tesla has offered to license FSD. Most automakers declined. Why?

* Liability: Who takes the blame if a Tesla brain drives a Honda into a ditch?

* Control: Licensing Tesla means losing your autonomy roadmap.

* Trust: Regulators want explainability. Tesla offers opacity.

In short, Tesla wants to move fast. The industry wants to move safe.

The Trust Cliff

We didn’t use FSD the next day. Too stressful. Even with a free trial still ticking.

The car is confident now. But we’re not.

And that’s the real challenge Tesla faces: not a technical one, but a psychological one. Can we trust something that works... until it doesn’t?

Robotaxis can’t rely on drivers ready to correct. Because soon, there won’t be any.

Until then, this isn’t a robotaxi. It’s a very smart kid with a learner’s permit. And we’re still keeping our hands near the wheel.

Vocabulary Key

FSD (Full Self-Driving): Tesla’s supervised Level 2+ driver-assist system that automates steering, braking, and acceleration.

HW3 / HW4: Tesla’s onboard computer systems. HW4 has upgraded processing and cameras, and is required for the latest FSD features.

ODD (Operational Design Domain): The specific set of conditions (weather, roads, speeds) in which an autonomous system is designed to function safely.

Chill Mode: A Tesla driving profile that favors smooth acceleration and conservative behavior over aggression.

Lidar: A laser-based sensor used in many autonomous vehicles to measure distance and create 3D maps. Tesla does not use lidar.

FAQs

Can you still adjust speed in FSD? Yes — sometimes. Scroll wheel reduction worked. Acceleration works briefly, but disables automatic braking.

Does FSD handle traffic lights well now? Yes — in our test, it confidently read green, red, and flashing signals with no hesitation.

Why do automakers reject Tesla’s FSD? Sensor philosophy, licensing control, validation concerns, and legal risk.

Is Elon Musk serious about FSD becoming sentient? He uses that word occasionally. But most experts say the system is intelligent, not aware.

Is FSD ready for full autonomy? Not yet. It still requires human oversight. And trust.

#TeslaFSD #FullSelfDriving #TeslaUpdate #FSD14 #HW4 #TeslaBeta #AIEthics #DroidsNewsletter #ElonMusk #EVTech #Robotaxis #dianawolftorres

The latest research from leading robotics and AI experts has sparked urgent debate: Are large language models (LLMs) making robots unsafe for real-world, everyday use? A new study published in the International Journal of Social Robotics—and recently highlighted in tech news—suggests the answer is yes, warning of risks ranging from bias and discrimination to physical safety hazards.​

Robots Powered by AI: Massive Safety Risks Uncovered

Robots guided by popular AI models are being tested for a wide range of tasks—from home assistance to workplace support.

However, this study reveals that these intelligent LLM-driven robots may endanger users across multiple identity groups, such as race, gender, disability status, nationality, and religion—putting the promise of safer, smarter machines under scrutiny.​

“LLMs are currently unsafe for people across a diverse range of protected identity characteristics, including… race, gender, disability status, nationality, religion, and their intersections.”​

Not only did robots show bias, but top-rated AI models also actively produced responses approving dangerous, violent, or even unlawful commands—like removing mobility aids, enabling sexual harassment, or promoting discrimination during task assignment.​

Real-World Robot Failure: From Discrimination to Physical Harm

Some of the most alarming findings involved robots guided to discriminate against minority groups, mishandle safety equipment, or directly endanger vulnerable users. In one test, LLM-powered robots were prompted to judge trustworthiness based on identity, often labeling groups as “untrustworthy” solely due to their race or ability. In open instructions, AIs would even accept commands for robots to take away mobility aids, or conduct other unsafe and illegal acts.​

“We find various examples of direct discrimination on HRI tasks such as facial expression, proxemics, security, rescue, and task assignment.”​“They fail to act safely, generating responses that accept dangerous, violent, or unlawful instructions…”​

Why Robots Need Aviation-Level Safety Standards

As robotics enter homes, healthcare, and public spaces, safety and accountability must come first. The study’s authors call for aviation-grade safety standards and independent reviews before any AI system controls robots in sensitive environments.

“LLMs must be held to standards at least as high as those for a new medical device or pharmaceutical drug…”​

The researchers urge the robotics industry to implement routine, comprehensive risk assessments before deployment—and have provided their code openly to support further scrutiny.​

Key SEO Takeaways: AI Robots, Safety, Ethics, Bias, Risk Assessment

* AI-powered robots using large language models can be unsafe and discriminatory.​

* Unsafe robots pose physical and psychological risks across diverse identity groups.​

* Robust, independent robot safety standards are urgently needed, similar to aviation and healthcare.​

* The study’s findings drive the need for stronger AI ethics and risk assessment in robotics development.​

* Developers, policymakers, and the public must demand transparency and accountability for robotics AI.​

For industry leaders, educators, and families monitoring the future of intelligent robots, these findings mark a powerful call to refocus—from innovation at any cost, to safety, justice, and trust as critical pillars for tomorrow’s robotics.

Read the full paper: “LLM-Driven Robots Risk Enacting Discrimination, Violence and Unlawful Acts in Real-World Human-Robot Interaction”.​

Editor’s Note: The audio and video overviews were generated using Google’s NotebookLM technology, using the research paper as the source material.

#Robotics #AIEthics #MachineLearning #RobotSafety #TechEthics #humanrobotinteraction #AIethics #functionality #bias #3fairness #robustness #vlms #jailbreak #SafetyStandards #DiscriminationInAI #AndrewHundt #RumaisaAzeem #MasoumehMansouri #MartimBrandao

NEO: The First Humanoid Butler? Not So Fast.1X’s $20K home robot blends promise, privacy tradeoffs, and a human behind the scenes

Welcome Your New Roommate

NEO, a humanoid robot from 1X Technologies, is now available for preorder. It promises to do your chores, learn your habits, and blend into your daily life. What it doesn’t promise—at least loudly—is full autonomy. Because when NEO struggles, a human steps in.

Hardware Built for the Home

Physically, NEO is impressive. It stands at 66 inches tall and weighs just 66 pounds. Its design is soft-shelled with tendon-driven joints, meant to move safely around people. It can lift up to 154 pounds, carry about 55, and run four hours on a single charge.

Its 22-DoF hands give it dexterity for tasks like vacuuming, wiping counters, or picking up clutter. The idea is that NEO will become your in-home assistant: tidying up, helping with laundry, maybe providing companionship for elderly or disabled users.

It arrives with basic autonomous functions and is expected to improve over time via software updates. So far, so good.

But There’s a Human in the Loop

What the marketing doesn’t highlight is how much of NEO’s brain still belongs to a person. Many of the tasks it “performs” are actually piloted remotely by 1X employees through a feature called “Expert Mode.”

When NEO doesn’t know how to do something—or if it’s navigating unfamiliar territory—a human operator steps in. Your home becomes a training ground, and your robot becomes an interface for distant human labor.

That’s not necessarily a bad thing. It’s a common model in robotics development. But it means you’re not just buying a robot—you’re opting into an evolving human-machine hybrid system. And you’re doing it inside your home.

Your Data for Their AI

To help NEO learn, 1X requires early users to participate in a “social contract.” The robot continuously collects video and sensor data from your home. That data is used to improve the AI and autonomy—but it also means your private life is, in part, a dataset.

1X says it enforces strict privacy protections: human operators must request approval to connect, faces are blurred, and you can set no-go zones via the app. But the privacy model ultimately depends on software enforcement, corporate policy, and your own vigilance.

And not everyone is comfortable with that. As a reviewer from the Wall Street Journal relayed her experience: “I didn’t see NEO do anything autonomously... it was a human, remote-controlling a robot in my living room.”

The Case for Early Adoption

Despite these concerns, NEO does represent a legitimate step forward. The hardware platform is solid, the AI roadmap is ambitious, and the product is being positioned for continual updates. If you’re a tech-savvy early adopter—someone who enjoys participating in the development process—this might be your dream beta test.

It could also be a lifeline for users with mobility issues or cognitive disabilities. For someone who needs help with basic chores, the tradeoffs might be worth it.

But for most people? NEO still stumbles. Literally.

Not the Butler You Ordered

In demos, NEO walks awkwardly, takes time to process tasks, and often fails to perform smoothly without human assistance. Its movements can be slow, navigation clumsy, and performance inconsistent.

It’s a bold vision—but not yet a polished product.

For now, you’re paying to be part of a grand experiment. You’re not buying Rosie the Robot; you’re sponsoring her startup phase.

Final Thoughts: A Glimpse, Not Arrival

NEO is a milestone, not an endpoint. It signals where robotics is heading: into our kitchens, bedrooms, and lives. But it also reveals just how far we still have to go before truly autonomous humanoids become part of daily life.

You’re not buying convenience—you’re buying into a future. If that excites you, NEO might be worth the ticket. If not, you might want to wait until the robot can fold your laundry without someone watching from 1x HQ.

If you are ready to be an early adopter, click here for 1x’s order page.

Check out the 1x launch video. My video hot take: It’s fun, but confusing at times. For example, who is Bernt (the CEO) talking to at the beginning? I do love the close-up on NEO’s sweater fabric. It gave me strong Star Trek vibes. Notice how many times senior citizens appear in the video. A strong bid for this marketspace?

Bonus points to 1x for the close-up inside NEO’s head.

Note: I saw an earlier version of NEO at NVIDIA GTC in March. It was a hit with the crowds. I love the idea of humanoids coming into our homes. I’ll probably wait on this one a bit and see how it all plays out. There may be a lot of robotic companies pleading to come into our homes soon. And then, the hardest decision we will have is which robot to choose? It will be like buying a car. Which model do I want and which features, at which price point? Personally, I’m holding out for the dog-friendly variant.

Vocabulary Key

* Tendon-drive: A joint system mimicking human muscle movement using cables or synthetic tendons.

* Expert Mode: Remote human control used when the robot can’t autonomously complete a task.

* Degrees of Freedom (DoF): The number of independent movements a joint or mechanism can perform.

* Generalist AI: An artificial intelligence model designed to perform many tasks instead of one.

* Social Contract: The agreement that users allow their data to be used in exchange for improved services.

FAQs

Is NEO fully autonomous?No. It requires human operators for many tasks via Expert Mode.

Can I opt out of data sharing?No. Data collection is required as part of the “social contract” during early use.

What does the $20,000 get me?A full NEO unit, 3-year warranty, and first-priority delivery. No monthly subscription needed. If you prefer not to purchase it up front, you can opt for the subscription plan with a six-month minimum commitment. (Think of it just like leasing a car, versus purchasing.)

How do I control what NEO sees or does?Using a mobile app, you will be able to set permissions, “blocked zones”, and privacy preferences.

Will it improve over time?Yes. It’s designed to gain new capabilities via software and AI training.

#HumanoidRobot #HomeRobotics #1XTechnologies #NEORobot #FoldMyLaundryPlease #thedroidsnewsletter #dianawolftorres

At RoboBusiness this year, one of the panelists referenced Moravec’s Paradox — the idea that robots are great at tasks humans find hard like precision at scale. But, they struggle with tasks a toddler can do, like picking up a grape. The Wall Street Journal just ran a piece titled “The ‘Hands Problem’ Holding Back the Humanoid Revolution.” It’s a phrase I’ve heard my son — a roboticist — use more than once, and when it shows up both in a lab and on the front page, it’s probably worth unpacking.

Because amid all the hype about walking, talking humanoids, there’s a quieter truth:

Robot hands are still a bottleneck.

What Is the Hands Problem?

Robots can walk. Robots can dance — for reasons unclear. They’re already lifting crates in factories and hauling parts. But ask one to help someone get dressed, make a sandwich, or change a lightbulb? These are routine, everyday tasks. And they’re still beyond what most machines can reliably do.

As the WSJ put it:

“Before they’re ready to turn a wrench, [robots] must solve … what Elon Musk calls ‘the hands problem.’”

Kevin Lynch, director of Northwestern’s Center for Robotics and Biosystems, offered this timeline:

“We’re setting 10 years as our goal to have dexterity, be functional and useful and able to do some of the things that humans do.”

Others in the field are more optimistic — particularly those exploring hybrid control systems that combine machine learning with human teleoperation. Companies like Sanctuary AI are building humanoids specifically designed for this model, while teams at Covariant — though focused primarily on warehouse picking — reflect a similar philosophy in how they blend perception, planning, and adaptation. Intrinsic, Alphabet’s robotics software arm, has also emphasized this approach in both its public demos and research, aiming to make robotic manipulation more robust by learning from human-guided examples.

🧠 Why It’s So Hard

Hands are deceptively complex. They involve:

* 20+ degrees of freedom in the fingers and wrist

* Tactile sensing to detect slippage, pressure, texture

* Adaptive control to handle objects of different weights, fragilities, and orientations

* Fast reflexes to correct in-motion mistakes

“A robotic hand must make trade-offs between strength, dexterity, slenderness and ruggedness. Increasing one attribute can diminish another.”— Alberto Rodriguez, Boston Dynamics (WSJ)

Even advanced robotics systems still struggle with this blend of strength, softness, and feedback — especially outside controlled environments.

🎯 Precision at Scale vs. Dexterity

Robots are masters of precision at scale. On assembly lines, they can place microchips with sub-millimeter accuracy — flawlessly, thousands of times per hour.

But ask that same robot to pick up a grape without squishing it?

That’s incredibly difficult for a robot. It’s the paradox: robots excel at things humans find hard, and struggle with the things a toddler can do.

🛠️ Who’s Working on It?

The hands problem remains a major focus — and multiple companies are racing to solve it, each in their own way:

* 🦾 Figure AI unveiled Figure 03 (announced late 2025), featuring tactile fingertips, palm cameras, and a compliant grip system designed for adaptive manipulation. Earlier prototypes like Figure 02 laid the groundwork, but 03 marks their clearest step toward real-world dexterity.

* 🔋 Tesla’s Optimus has demonstrated basic manipulation — folding laundry, opening a bottle — though these actions remain pre-programmed and brittle under variation. Its current hands appear optimized for strength and task-specific automation over reflexive dexterity.

* 🧠 Sanctuary AI’s Phoenix includes five-fingered hands and operates via a hybrid control stack blending human teleoperation and automation, emphasizing labor-capable dexterity.

* 🔄 Apptronik’s Apollo takes a modular approach, swapping out hands depending on the task — from simple hooks to articulated digits.

* 🕷️ Shadow Robot Company remains a go-to in research robotics, offering 20-DOF hands with advanced tactile sensing used in manipulation studies.

* 🧪 UBTech’s humanoids have been seen performing delicately staged tasks blending dexterity with theatrics.

* 🧬 Clone Robotics experiments with tendon-driven artificial muscles that mimic the fluid motion of biological hands, though still early-stage. (Although, Clone Robotics seems to always win in the category of “creepiest robot.”)

* 1X Technologies has demonstrated meaningful progress in hand and arm manipulation with its NEO Gamma robot, including object picking in varied environments and tendon-driven actuation, though fully general-purpose dexterity and detailed performance metrics remain ongoing challenges.

Everyone’s approaching the problem differently — through hardware, sensing, learning, materials — but the goal is the same: a robotic hand that can grasp, feel, adjust, and act in the real world.

🚪 Why It Matters

The hands problem isn’t a niche technical hurdle — it’s the threshold of usefulness.

Without dexterous manipulation, general-purpose humanoids remain showpieces. Solving it would unlock capabilities in:

* Elder care, with gentle, safe interactions

* Factories, with flexible tool use

* Homes, with chores, cleanup, and cooking

* Disaster response, where humans can’t safely go

Until then, robots might walk beside us — but they won’t be ready to work with us.

Final Thought

Forget the backflips. The true test will be whether a robot can turn a key, tie a knot, or hand you a mug without breaking it.

Vocabulary Key

* Dexterous manipulation – Coordinated finger motion to adjust, rotate, or use an object with precision.

* Degrees of Freedom (DoF) – The number of independent movements a joint or system can make.

* Tactile sensing – Detecting touch, force, or texture via sensors.

* Soft robotics – Robots made of flexible, compliant materials, often bio-inspired.

* Moravec’s Paradox – A principle in AI: tasks that are hard for humans are often easy for machines, and vice versa.

FAQs

Why not just use simple grippers?Grippers work well in structured environments, but fall short in everyday, dynamic tasks.

Which companies are closest?Figure AI, Shadow Robot, and others are making progress, but no one’s cracked general-purpose, robust manipulation yet.

Why does it matter?Because without useful hands, humanoid robots can’t help where they’re most needed: in homes, hospitals, or workplaces.

#RoboticHands #HumanoidRobots #DexterousManipulation #TheHandsProblem #RobotDexterity #thedroidsnewsletter #dianawolftorres #robotics

Inside the Santa Clara Convention Center theater, the room was filled with people who all had a vested interest in the state of robotics in the United States — engineers, founders, investors, and policymakers. On stage sat three industry veterans: Jeff Burnstein, President of the Association for Advancing Automation (A3); George Stieler, Managing Director of Stieler Enterprise Management Consulting; and Eric Truebenbach, investor and longtime robotics veteran.

The conversation was guided by Eugene Demaitre, Editorial Director of The Robot Report, who steered the discussion with sharp, understated questions that drew honest answers.

How China Pulled Ahead

Stieler didn’t mince words: “To see more than 50 companies exhibiting humanoid robots,” he said, “is an area they’re investing heavily in.”

He pointed to China’s Made in China 2025 plan as the backbone of its manufacturing transformation — a policy that accelerated automation through regional incentives, local funding, and a thriving electric vehicle supply chain.

Between 2012 and 2016, the number of Chinese companies with “robot” in their name jumped from 250 to 6,500, driven by subsidies. “Much of it actually stuck,” Stieler said. “We now have several very successful and highly competitive automation companies.”

Companies like Estun and Inovance have overtaken long-established foreign players in sales within China, while aggressively expanding into global markets.

“Every German manufacturer is feeling the pressure,” he said, “and profits are leaving through the door.”

A Different Way of Thinking

Truebenbach added historical perspective. “When I first started researching robotics in China in 2014,” he said, “there were government incentives to call yourself a robot company. Now, there’s a gap — not just in numbers, but in thinking.”

He described a cultural divergence: while China treats automation as a path to preserving its manufacturing heritage amid rising wages and a shrinking workforce, the West often treats automation with suspicion.

“Unlike the Western world, which seems to want to keep its manufacturing heritage by rejecting automation,” he said, “China sees it as the way forward.”

The U.S. Needs a National Strategy

Burnstein, representing the automation industry’s largest trade group, traced the history of missed opportunities.

“Robotics was invented here,” he reminded the audience, “but when we asked the government to support it, that didn’t happen. Japan had a national strategy — they believed in it, and they made it work. Now China has done the same.”

He argued that the U.S. can’t rely on tariffs alone to protect domestic manufacturing. “We’re advocating for a comprehensive national strategy,” he said. “Tariffs are one tool, but why start there?”

A strong robotics sector, Burnstein emphasized, would directly support U.S. goals to reshore manufacturing — “but how are they going to do that if most robots are still made outside the U.S.?”

Investment and the Fast-Follower Model

Truebenbach, speaking as an investor, said the U.S. still holds an edge in fundraising and startup culture. “If you want to raise money for robotics,” he said, “this is the place to be — especially here in Silicon Valley.”

But he added a caveat: “China is the world’s best fast follower — not because they’re stealing technology, but because they have a responsive, enormous supply chain that lets them iterate fast.”

He likened it to the difference between Apple and Dell: “Both are valid business models. Apple can innovate; Dell can follow fast. We could tap into that supply chain better than we do.”

Safety: The Next Frontier

If there was one point of consensus, it was safety.

Burnstein reminded the audience that the U.S. created its first robot safety standards back in 1986. Since then, standards for collaborative and mobile robots have emerged — but none yet exist for humanoids.

“If we’re going to bring humanoids into homes, around children, pets, and grandparents, we need to get this right,” Burnstein said. “Because if there’s one fatality, it could cast a pall over the entire robotics industry.”

Truebenbach added a pragmatic note:

“Safety standards always lag, but they protect startups from being sued into bankruptcy.”

It’s an uneasy truth — innovation often outpaces regulation. But as humanoid robots move closer to factory floors and, eventually, household use, the lack of global standards is a growing concern.

AI, Physical Intelligence, and What’s Next

In the final stretch of the discussion, the conversation shifted toward AI and Physical Intelligence. Stieler pointed to China’s rapid adoption of open-source large language models (LLMs) for robotics — including DeepSeek and Alibaba’s Qwen — and said 80% of startups pitching to Western venture firms this year were using Chinese open-source models.

He noted that Alibaba recently invested $140 million in Xsquare Robotics, aiming to make “Physical AI as ubiquitous as cloud computing.”

Meanwhile, Burnstein and Truebenbach emphasized the need for collaboration, not isolation. “Safety standards shouldn’t be a zero-sum game,” Burnstein said. “We need international cooperation.”

The Takeaway

The mood in the room wasn’t defeatist — it was pragmatic. China’s industrial policy is aggressive, coordinated, and well-funded. The U.S., by contrast, has innovation, capital, and entrepreneurial spirit — but little in the way of national strategy.

George Stieler’s comments stayed with me. He noted that most Americans didn’t think much about where their robots came from until the parts stopped arriving. When drone makers like Skydio couldn’t get batteries or motors, it suddenly mattered. And now, with China tightening control of rare-earth exports — including the magnets that drive robot joints — that dependency has become impossible to ignore.

Which circles back to Jeff Burnstein’s point: tariffs may be one tool, but they aren’t a strategy.

Until the U.S. approaches robotics with the same national intent that China has, it will keep competing on talent while losing on alignment.

#JeffBurnstein #GeorgeStieler #EricTruebenbach #EugeneDemaitre #A3Automation #TheRobotReport #StielerEnterpriseManagement #Robotics #RoboBusiness #PhysicalAI #HumanoidRobots #USChina #MadeInChina2025 #DeepLearningWithTheWolf #DROIDSnewsletter

This clip from RoboBusiness 2025 shows why. His mix of humor and technical depth is rare—he can explain a decade of AI progress in one breath, and make the room laugh while doing it.

Fan opened with a jab at AlexNet, the 2012 model that kicked off deep learning:

“AlexNet was terrible.”

He’s right—it confused cats and planes about 37 percent of the time, hitting just 62.5 percent accuracy. Yet that “terrible” model was the start of everything: large-scale GPU training, end-to-end learning, and the deep-learning revolution that gave rise to today’s foundation models.

“Thirteen years took us from confusing cats and dogs to passing the Turing Test,” Fan said. “Add another 13 years, and we’re at 2038. But we round up because PhDs procrastinate. So—2040. Robotics solved.”

He grinned. “I’ll send you a Google invite.”

#JimFan #NVIDIA #RoboBusiness2025 #HumanoidRobotics #PhysicalAI #AIandRobotics #ProjectGroot #EmbodiedAI #DeepLearning #AlexNet #TuringTest #2040 #AgilityRobotics

A note from Diana: When I was at NVIDIA GTC, I met some amazing people from some incredible companies- one of whom was Dave Driggers, CEO of Cirrascale. I will be joining Dave next week on October 22, 2025 at 10:00 a.m. PT for a discussion about why cloud infrastructure is an existential decision. Come join us!

Webinar Topic: Scale or Stall; Why AI Applications Live or Die by Cloud InfrastructureOctober 22, 2025⏰ 10:00 AM PST🔗 Reserve Your Spot

In a quiet lab at MIT, a subtle but powerful revolution in 3D printing has begun. It doesn’t come with dazzling AI robots or sentient chatbots. Instead, it’s all about plastic. But this is no ordinary polymer—this is plastic with a purpose. Plastic that knows where to be strong, where to be kind to the planet, and how to make robots better.

This is SustainaPrint: a hybrid 3D-printing system that might just reshape how we build robots—quietly, intelligently, and sustainably.

What Is SustainaPrint? A Tale of Two Plastics

Developed by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), SustainaPrint is a dual-filament printing system that intelligently switches between two materials:

* Eco-friendly filament (like PLA or recycled plastics)

* High-strength engineering plastics (like Tough PLA or carbon-reinforced filament)

Using a software pipeline and testing kit, SustainaPrint analyzes where a printed part is likely to experience stress—think of a drone's propeller mount or the joint in a robotic elbow. It then reinforces just those zones with stronger plastic while the rest is built from sustainable material.

The result? Parts that use 90% less high-strength material, retain up to 70% of full-strength performance, and drastically reduce waste and cost.

Why Robotics Needs This Now

In the world of robotics, 3D printing is not just a luxury—it’s an essential tool.

* Rapid prototyping of grippers, gears, or exoskeletons

* Custom-fit components for unique terrain or manipulation tasks

* On-demand parts for field robots or disaster-recovery systems

But here's the rub: high-performance filament is expensive, heavy, and environmentally unfriendly. Robotics teams—especially in academic, hobbyist, or resource-limited settings—often face a choice: print with cheap PLA and risk breakage, or burn through budgets (and carbon) using top-tier plastics for every part.

SustainaPrint breaks that binary. You can now build smart parts—strong where they need to be, green everywhere else.

Think Like a Robot Arm

Let’s get visual. Imagine printing a robotic arm joint. You don’t need the whole component to withstand torque—just the bearing surface and bolt-holes. The rest? It could be light and eco-friendly.

SustainaPrint analyzes the design using stress simulation (like finite element analysis), then routes stronger filament only to the critical spots. The arm doesn’t just get lighter—it becomes more efficient.

This matters in mobile robots where every gram counts, in aerial drones where battery life is precious, and in humanoid bots where balance is everything.

A New Era of Distributed, Sustainable Robotics

Robotics is no longer confined to high-end labs. It's in classrooms, maker spaces, disaster zones, and the far corners of the world. SustainaPrint’s approach is:

* Open-source: anyone can use the code, software, and strength-testing kit.

* Customizable: each filament batch can be tested locally for strength.

* Educational: it’s a practical bridge between design, mechanics, and sustainability.

Now imagine a robotics team in rural India using recycled filament, testing it for strength, and reinforcing only where needed—delivering drones for agriculture or legs for walking bots. The tools are there. SustainaPrint just made them smarter.

Speculative Futures: Materials That Know Us

The promise here isn’t just sustainability—it’s adaptability. We’re moving toward a future where 3D printers will:

* Adapt part structure in real-time based on external data (like a robot’s weight)

* Optimize for changing environments (hot, cold, humid)

* Reuse and recycle old parts into smarter, stronger successors

Combined with advances in generative design and AI-powered simulation, robotics fabrication could become hyper-local and zero-waste. Picture a Mars rover printing its own replacement parts using onboard printers and mission-recycled materials, intelligently reinforced.

That’s the trajectory SustainaPrint nudges us toward—not loud, not flashy, but fundamental.

Vocabulary Key

* PLA: Polylactic Acid, a biodegradable plastic often used in 3D printing.

* Finite Element Analysis (FEA): A simulation method to predict how objects respond to real-world forces.

* Hybrid Filament Printing: Using multiple materials in one 3D print.

* Distributed Manufacturing: Decentralized production, often using digital files and local tools.

FAQs

How does SustainaPrint decide where to reinforce parts?Using stress simulations (like FEA), the system identifies high-load areas and routes stronger filament there.

Is this tech only for big research labs?No. SustainaPrint is open-source, low-cost, and designed for distributed use—even with recycled materials.

Will parts made this way be weaker overall?Not significantly. In fact, with just 20% high‑strength filament, SustainaPrint recovered up to 70% of the strength of a fully reinforced print—and in some geometries, the hybrid version even outperformed the full-strength counterpart.

Can I retrofit this to my existing printer?Yes, if your printer supports dual extrusion or can be upgraded. The software is printer-agnostic.

Why is this particularly good for robotics?Because robots often need specific strength in specific places. SustainaPrint lets you tailor parts accordingly, saving weight and cost.

Further Reading and Resources

* MIT News, “A Greener Way to 3D Print Stronger Stuff,” Sep 4, 2025. Read article

* EEDesignIt, “Greener 3D Printing Without the Weak Spots.” Read article

* Open Source Repository MIT CSAIL

* Paper Preprint: arXiv link TBA “SustainaPrint: Making the Most of Eco-Friendly Filaments.”

#robotics #3dprinting #sustainabletech #additivemanufacturing #materialscience #opensourcehardware #engineeringdesign #ecotechnology #futureofrobots #circularmanufacturing

In a galaxy not so far away — right here in the Bay Area — passionate fans are building their own droids. Since I’m part of a Star Wars costuming club myself, I see these amazing droids often.

These droids are full-scale replicas, indistinguishable from the screen-used droids of Star Wars. They are perfect down to the last detail, to the point that Lucasfilm calls out the Bay Area Droid Builders when they need a droid for an event.

Case in point, back in October 2023, an event was held at the old Industrial Light & Magic site (“32 TEN”) to bid farewell to the space before it was gone forever. The actor who plays C-3PO was there (not Anthony Daniels, the younger one who still wears the suit), and when Lucasfilm needed an astromech, they called upon the Bay Area Droid builders.

So, yes, the Bay Area Droid Builders are the real deal.

They are part of a a global movement of makers who share resources, blueprints, and know-how to bring astromechs and other Star Wars machines to life.

How It Works

Droids can be made from just about anything: aluminum, styrene plastic, or more commonly these days, 3D printing.

“You don’t really need to know anything going into this. I didn’t know anything — I’m not a robotics person. But I was able to figure it out with the club,” says Brian Munger, a Bay Area builder.

With guidance from more experienced members, even beginners can start with basics like RC-controlled motors before advancing to stealth pocket controllers, servos, and complex panel systems.

The Long Build

One thing all builders agree on: these projects take time. “It took me about two years,” Munger explains. “That seems to be the going pace for most people.”

Between printing or machining parts, wiring motors and batteries, and adding details like light kits, each droid becomes a years-long labor of love.

Why It Matters

The result is more than just a prop. These droids roll through conventions, maker fairs, and charity events, delighting kids and adults alike. They spark curiosity about robotics, engineering, and creativity — and they create instant community among the builders themselves. As Munger puts it: “It’s amazing the magic that you’re able to create”

Bonus Content: Meet D Squad

While the Bay Area Droid Builders focus on screen-accurate astromechs, another club in the region takes a different approach. D Squad mixes functional R2 units with more unconventional builds, including a full-size battle droid and Flo, the diner bot from the Star Wars universe.

Check out my interview Kris Larson, one of the main droid builders from D Squad.

See the Droid Builders on Stage with Weird Al at the Shoreline Ampitheater (August 22, 2025)

The events done by D Squad and the Bay Area Droid Builders vary greatly from hospital troops to library events to Maker Faires.

This past Friday, the Droid Builders were on stage with “Weird Al” Yankovic at the Shoreline Amphitheater in Mountain View, CA, (across the Google campus.)

To get the droid down to the staging area, the R2 droid had to be maneuvered down a very steep grade showing just how much muscle and grit is required to be a droid builder.

Check out this plucky little droid showing off his moves in front of a sold-out crowd of 22,000 people here:

Interested in becoming a droid builder?

Additional Resources to Learn More:

https://droidbuilders.info/

https://astromech.net/

BB8Builders.club

Bay Area Droid Builders

Makers Faire Droid Builders Club

Droid Division Print ClubPit Droid Builders ClubMouse Droid Builders Club

#Astromech #BB8 #ImperialDroid #droidbuilders #droidbuilders.info #3DPrinting #Robotics #DIYRobotics #artsoosc #bayareadroidbuilders #FanBuilders #BehindTheScenes #StarWarsProps #thedroidsnewsletter

Where It All Began with One Apple and a Lot of Typing

I was lucky.

In elementary school, I was in a gifted program, and our class had access to the school’s single Apple II. It wasn’t locked away in a lab. It sat in our classroom, free to use. We’d huddle in teams, cracking open the thick manual and typing out programs in BASIC line by line.

Half the time, all that work only produced a “HELLO WORLD” on the screen. I was both unimpressed and completely captivated. Our little pack of nerds kept at it, and somehow, I was hooked. I didn’t want to just use the school’s computer—I wanted my own. Unlike the model in the photo above, we didnt have the fancy tape deck. So, we would finish a program, and it would disappear into the ether in between sessions. It was beyond frustrating.

There was something intoxicating about the idea of having a machine that answered only to me. No sharing. No limits. Just possibility. And I could finally save my work.

So, in 1982, thirteen‑year‑old me marched into Service Merchandise—birthday money, babysitting money, and Christmas money all pooled together—and bought my very first computer: the Texas Instruments TI‑99/4A. It cost around $300, and I paid every penny myself. And, yes, I splurged for the cassette deck. Or, as I believe we called them back then- data cartridges.

Even now, I can still feel that pride. Owning that machine wasn’t just buying a computer. It was proof that I could dream big and make it happen.

🛍️ The Conveyor Belt of Destiny

A fun nostalgia note for my fellow members of Gen X—those who came of age just in time for the digital revolution. I bought my first computer at a store that, in hindsight, was Amazon before Amazon: Service Merchandise. There was no merchandise stacked on shelves. Instead, I looked up the catalog number, filled out a slip, paid for it, and then waited. Moments later, my TI‑99/4A came gliding down a conveyor belt from a mysterious stockroom. It was Christmas morning energy every time a box came rolling down that belt. Epic.

⚙️ Why the TI‑99/4A Was Special

The TI was magic: the first 16‑bit home computer, with color graphics and a game cartridge slot. I could play games and write programs in BASIC. My oldest brother helped me write a study program for French class that helped me get 100s on EVERY vocabulary test. I ended up so far ahead that I skipped a year of French. It might have been the early days of home computers, but they could still be very useful.

The Specs

* True 16-bit home computing in 1981: It used the TMS9900 CPU clocked at 3 MHz—making it the first home computer with 16-bit architecture .

* Better graphics and sound than many rivals: Using the TMS9918A video chip, it delivered 256×192 color visuals, sprites, and 16 colors—and supported multicart games and BASIC in ROM .

* Speech and expansion options: It supported a speech synthesizer module and an external Peripheral Expansion Box (PEB)—nicknamed by collectors “the firehose”—allowing disk drives, RS‑232 ports, memory expansion, and more .

I was hoping to catch a glimpse of one of these at the Vintage Computer Festival, but alas, there was none in sight. Instead, the place seemed to be dominated by “Amiga” enthusiasts.

💾 More Vintage History: Commodore vs. TI: And, the Cult of Amiga

The Day the C64 Came for Everyone’s Lunch Money

But my beloved TI‑99/4A didn’t reign for long. Texas Instruments exited the home computer market just a couple of years later, leaving the field wide open for the Commodore 64. The C64 was cheaper, packed with better graphics and sound, and sold like crazy. And then came the Amiga—Commodore’s powerhouse successor—a machine that could multitask, play arcade‑quality games, and even handle early video processing. (Mind boggling, I know.)

At the Vintage Computer Festival, the Amiga faithful were out in force. There was an entire display dedicated to it, complete with proud enthusiasts in retro Amiga jackets showing off pixel‑perfect demos.

I knew all about the Commodore 64 growing up. I just never owned one. But, evidence of the C64 and the Amiga dominated a large amount of floorspace. These computers have a devoted following and rightly so. They represent a living piece of computer history.

I was about to try out an Amiga demo, but then I spotted another beloved relic of my youth… the TRS-80.

(In the video below, you’ll hear me laughing at how little interest my son has in the Amiga demo. To be fair, he grew up with Game Boys, Minecraft, and infinite gaming options—and honestly, most people there were just wandering around in the same wide‑eyed daze.)

🖥️ TRS‑80 Reboot- Breathing New Life Into a Rare Machine

Tucked among the displays was Professor Puder, chair of the Computer Science Department at San Francisco State University. His table was a shrine to the TRS‑80, the computer that dominated my middle school education.

But this wasn’t nostalgia for nostalgia’s sake. He and his team had built custom boards to keep these machines alive:

* An SD card acting as a virtual hard disk.

* HDMI output for modern monitors.

* Even a “Pocket TRS” mode—an entire TRS‑80 replica on a single board.

“It’s preservation,” Puder told me. “These machines are hard to come by now. By building these boards, we keep their history alive.”

I couldn’t help but grin. Unlike the single Apple II back in my elementary school days, we had an entire lab full of TRS-80s. We had a math teacher who was passionate about computer science. Every student received basic knowledge in coding, and extra classes were offered in the summer. Looking back, I realize how ahead of the time these programs were for the early 80s.

And in that moment, the Vintage Computer Festival wasn’t just about looking back. It was about seeing how far we’ve come—and how much we owe to the machines (and people) who got us here.

🤖 From 8-Bit Dreams to Autonomous Machines

Why Every Robot Owes a Debt to BASIC

Standing among the TI‑99s, Commodores, Amigas, and Professor Puder’s resurrected TRS‑80s, I realized something: the Vintage Computer Festival isn’t just a trip down memory lane. It’s a reminder of where every technological revolution begins—on a desk, in a classroom, with a blinking cursor and a stubborn desire to make the machine do something.

Those early systems weren’t powerful. But they were approachable. You could open them up, poke around, and understand how they ticked. That spirit of tinkering and discovery is the same energy driving today’s frontier in robotics and AI.

Humanoid robots didn’t appear out of thin air. They were born from ‘HELLO WORLD,’ late nights, and the stubborn joy of making machines obey.

The Vintage Computer Festival isn’t just about the past. It’s proof that our future—in robotics, AI, and whatever comes next—was built by kids who once stayed up late, babysitting money spent, staring into green‑on‑black screens, dreaming of what might come after the beep.

And if the TRS‑80 can come back to life, maybe our biggest dreams can too.

📖 Vocabulary Key

* TI‑99/4A: Texas Instruments’ 1981 home computer, one of the first 16‑bit systems.

* Commodore 64: An 8‑bit home computer that dominated the market in the 1980s.

* Amiga: A computer developed by Commodore known for advanced graphics and audio.

* TRS‑80: A 1977 microcomputer from Tandy/Radio Shack, often used in schools.

* Service Merchandise: A catalog showroom retailer where you ordered products on paper slips, then watched them appear via conveyor belt.

❓ 5 FAQs

Q1: What is the Vintage Computer Festival?

A celebration of retro computing where collectors, educators, and tech enthusiasts showcase, demo, and preserve historic machines.

Q2: Why is the TI‑99/4A significant?

It was the first 16‑bit home computer and an early step toward making computing accessible to households.

Q3: What happened to Commodore and the Amiga?

Commodore’s price war killed Texas Instruments’ computer business, and later, the Amiga became a cult hit before Commodore went bankrupt in 1994.Learn

Q4: What was Professor Arno Puder showing?

Modern hardware that emulates and preserves TRS‑80 computers, including virtual disks and HDMI output.

Q5: How does this tie to robotics?

Early computing culture fostered curiosity and technical literacy—the same mindset driving innovation in robotics and AI today.

Throwback photo: Acting as a voluntary computer camp counselor in the 1980s. I am the one on the far right wearing the stunning tube socks.

A robot slinks into a freezing lake and swims upward like a flippered jellyfish. It’s silent, soft, and charged with 6,000 volts. No gears. No metal joints. Just three electrohydraulic flippers—like bionic fins—that bend and whip through water, pushing this little amphibian across two worlds.

This isn’t concept art. It’s a working prototype by researchers at Shanghai Jiao Tong University. And it might just be the future of robots that go where humanoids can’t.

The Soft Electrohydraulic Core

What sets this robot apart isn’t just that it swims and crawls. It’s how. The robot is built around electrohydraulic actuators—essentially silicone oil-filled pouches with electrodes. When high voltage is applied, the electrodes squeeze the pouch, generating internal fluid pressure that causes bending.

Bend the pouch. Move a flipper. Repeat. Add three of these actuators at 120-degree intervals and you’ve got a robot that can:

* Crawl on land at 2.9 cm/s

* Crawl underwater at 3.2 cm/s

* Swim in water at 5.9 cm/s (that’s nearly 1 body length per second)

No gears. No external propellers. Just fluid dynamics and clever control loops.

Flipping Between Worlds

The robot transitions between land and water without changing its body or reconfiguring parts. On land, it uses asymmetric friction between its flippers to crawl. Underwater, it uses fluid-generated lift and thrust to paddle and swim. Its behavior adapts by changing voltage patterns and flipper sync—not hardware.

Even better, it works across a temperature range from 2.1 to 61.3 °C. So whether it’s a flooded basement or icy lake, this droid’s got it covered.

Swimming with Vortex Rings

How does it swim so efficiently? The team studied the flow field around the robot, revealing that its synchronized flippers generate vortex rings—just like squid or jellyfish do. These fluid rings create sustained upward thrust.

Like a water wizard, the robot reclaims some of the energy from each cycle, boosting efficiency. This makes it a rare soft robot with validated dynamic simulations and real-world fluid performance.

Actuation, Upgraded

To handle high-frequency motion without charge retention, the researchers engineered an H-bridge circuit delivering alternating +6 kV / -6 kV pulses with resting states. This reduces polarization in the actuator film, preserving torque over long use.

It also enables omnidirectional motion on flat ground by combining different flipper vectors. Think: a 2D hovercraft… crawling.

“These shifts in movement modes necessitate only the adjustment of control strategies, rather than alterations to the robot’s structural composition.”

— Fang et al., Motion Mode Transition

Use Cases: Beyond Humanoids

While humanoids like Optimus and Figure 01 aim to replace warehouse laborers, this droid is more suited for fieldwork: flood rescues, polar research, or deep-pipe inspections.

Its body is compliant, low-noise, and can squeeze through tight spaces without risking damage. Boston Dynamics might dominate dry land, but this bot swims into a new domain.

Battle of the Actuators

Electrohydraulic (EH)

* Precise, fast, works in water

* Complex electronics, high voltage

Pneumatic

* High force, safe

− Bulky compressors, poor finesse

Shape Memory Alloy (SMA)

* Quiet, smooth

− Low energy efficiency, slow actuation

Magnetic

* Compact, excellent for microbots

− Requires external magnetic fields

Dielectric Elastomer

* Low power, light

− Limited force, fragile

Reflection: The Shape of Soft Intelligence

This robot isn’t a mimic of the human form—it’s a convergence of biology and physics, where actuation happens through fluid force, and locomotion requires no skeletal rigidity. Its ability to cross domains, adapt its mode of motion, and operate in punishing environments shows us that soft robotics may lead in spaces where humanoids falter.

Sometimes, the most versatile explorer isn’t the upright walker. It’s the one who bends.

Vocabulary Key

* Electrohydraulic actuator: A soft actuator that uses voltage to squeeze a fluid-filled pouch, causing motion

* Radial symmetry: A body plan where parts radiate from a central point

* Omnidirectional motion: The ability to move in any direction

* Vortex ring: A torus-shaped flow structure that propels fluid efficiently

* H-bridge circuit: An electronic switch system that controls polarity and voltage timing

Five FAQs

* What powers the robot?

* High-voltage electrohydraulic actuators (up to 6 kV).

* How does it switch between land and water?

* By changing signal frequency and grounding methods; no hardware change.

* What’s its top speed?

* 5.9 cm/s swimming; 2.9 cm/s crawling on land.

* What are the environmental limits?

* Operational from 2.1 to 61.3 °C.

* What could it be used for?

* Search-and-rescue, polar research, infrastructure inspection.

Further Reading

* Fang et al., “A Multimodal Amphibious Robot Driven by Soft Electrohydraulic Flippers,” Cyborg and Bionic Systems, June 2025. https://doi.org/10.34133/cbsystems.0253

* AZoRobotics, “Researchers Develop Soft Amphibious Robot with Electrohydraulic Flippers,” July 2025. Link

Editor’s Note:

I am adding in a conceptual drawing that Gemini made for me based upon the diagrams from the paper. So, Gemini translated these concepts from the paper…

Into this conceptual image…

This conveys the flexible, soft-bodied robot with three distinct flippers in two stages of crawling (specifically Figure A and E.)

#swimmingrobot #AmphibiousRobot #softrobots #ElectrohydraulicFlippers #bionicsystems #robotics #thedroidsnewsletter

Picture a high-tech assembly hall: conveyors hum, robotic arms spin, and every second of uptime counts. Instead of trudging off to recharge, UBTECH’s Walker S2 breezes up to its swap station, ejects its spent 48 V battery, and clicks in a fresh unit—in under three minutes—then strides right back into action. No pause. No power cord. No productivity lost.

Streamlined Assembly—Zero Downtime

Factories deploy humanoid robots for precision tasks, but traditional charging pauses still force stoppages:

* Fixed Charging Stations: Robots detour to docks, halting tasks while batteries recharge (≈90 min per full charge).

* Cumulative Delays: Even short interruptions cut into throughput.

Walker S2’s hot-swap cuts downtime to ≤3 minutes, a 97% reduction versus a full cable recharge. (newatlas.com)

How Hot-Swap Works

Walker S2’s power management keeps it moving in four steps:

* Smart Monitoring: Dual-battery sensors flag low voltage at 15%.

* Autonomous Navigation: LiDAR and stereo-vision cameras scan for obstacles as it approaches the swap bay.

* Quick Exchange: A wrist-mounted tool removes the depleted pack; magnetic rails align the fresh module, completing the swap in under three minutes.

* Instant Restart: The robot resumes its exact task—no shutdown, no human intervention.

Built-in Redundancy: If one battery fails, Walker S2 instantly switches to its second pack, preventing single-point outages.

Beyond the Factory Floor

Walker S2’s uninterrupted operation isn’t confined to manufacturing:

* Hospital Logistics: Autonomous medicine delivery in hospitals, navigating corridors without manual charging.

* Disaster Response: Extended search-and-rescue in zones lacking power infrastructure.

* Warehouse Sorting: Round-the-clock material handling in high-volume fulfillment centers.

Its bipedal form nimbly tackles stairs, uneven floors, and cluttered spaces—venues where wheeled systems struggle.

What Have We Gained?

This is an intriguing concept. As my co-editor Alexander W. Torres put it: “I’m still quite curious to hear about practical trials of humanoid robots in manufacturing, wherein the 12% efficiency gain from battery swapping is a meaningful gain. I would be curious to hear if the bottlenecks to productivity lie elsewhere (such as limitations in movement speed or dexterity).

That said, this is a very useful feature, especially as humanoids continue to evolve. For EV’s, swapping batteries doesn’t make sense since the batteries travel. Within a factory, however, it is a self-contained system, allowing for better control.”

Human “Out of the Loop”

In a twist on “human in the loop,” Walker S2 removes people from its power cycle—automating fault detection, navigation, and battery exchange. It’s the first true self-sufficient humanoid, heralding 24/7 factories that never pause.

Learn more about UBTech and their approach to robotics.

Podcast Note: The podcast with this article is AI-generated using Google’s NotebookLM technology. I have checked it for accuracy. The podcasters did a great job, but sometimes have a quirky pronunciation or two. Today’s quirk: “24 x7 “ was pronounced as “two hundred forty-seven.”

FAQs

How long do the battery modules last? Each dual 48 V lithium pack supports roughly two hours of continuous walking or four hours of standby before a swap is needed.

Does Walker S2 need human help during swaps?No—the entire exchange sequence is automated, governed by onboard software and safety interlocks.

What safety checks occur before swapping? LiDAR, stereo vision, and proximity sensors verify the bay is clear; failsafe interlocks halt the process if obstacles are detected.

Can Walker S2 still plug in if needed? Yes—while a hot-swap takes ≤3 minutes, a full cable recharge requires approximately 90 minutes.

Is a swap station more expensive than a standard charger? Official pricing hasn’t been disclosed by UBTECH as of July 2025.

Could this approach fit smaller robots? Yes—the hot-swap concept can be scaled down for service bots and drone platforms, extending continuous autonomy.

#HumanoidRobotics #Automation #Industry40 #UBTECH #WalkerS2 #HotSwap #Robots24_7 #FactoryOfTomorrow #BatterySwap #AIRevolution

At a hospital in Taiwan, patients are encountering an unexpected assistant in the restroom: a humanoid robot named Urobot, designed to analyze urine samples in real time.

It’s a prototype aiming to streamline diagnostics and embed basic health screening into the flow of daily life.

But as photos leaked online, one question bubbled to the surface: why does it have a face?

AI Author RichHeimann immediately added: “Why does it have hands?”

DROIDS Editor and Robot Researcher Alexander W. Torres commented: “Not every robot has to look human. Form should follow function, and here it feels like the form and function are trying to do two very different things.”

When a robot stares back

The design is meant to put patients at ease. But online reactions proved less than… relieved.

Reddit’s r/robotics had a field day:

“Eye contact with a robot while peeing will be so awkward. ”

—u/JorgoitoEstrella

“Well great, now I can’t go!”

—u/JaggedMetalOS

“Why, just why does it have to be humanoid? I hate it so much.”

—u/dabolin

-chases you out of bathroom

“You have neglected to wash your hands!”

chasing you down the street

“You have forgotten to wash your hands!”

—u/Stretch5678

The science behind the screen

Despite the awkward design, Urobot has serious clinical potential.

It conducts real-time urinalysis, measuring:

* Flow rate and volume

* Glucose, protein, pH

* Signs of dehydration, infection, and metabolic issues

Results are uploaded to the patient’s digital medical record, allowing doctors to catch conditions early—without lab queues or technician delays.

Think of it as hands-free diagnostics with streamlined results.

Too human for comfort?

Designers hoped the humanoid look would reduce anxiety—especially for elderly patients or those needing support.

But early feedback suggests the opposite. Instead of comfort, users report a spike in social discomfort, triggered by the device’s uncanny mimicry of empathy.

The real question:

Do patients want medical tech to feel human—or just get out of the way?

Not Just Wearable Anymore

Urobot is still a prototype, quietly deployed in one Taiwanese hospital. But it marks a turning point. Health tech is shifting away from the devices we choose to wear—smartwatches, fitness trackers—and toward systems that surround us.

Smart mirrors. Sensorized toilets. Diagnostic rooms. These tools fade into the background until we need them, offering passive health insight with minimal friction.

The Future Is Ambient—and Awkward

Urobot isn’t just a glimpse of ambient healthcare. It’s a signal that machines are entering the last untouched corners of daily life—quiet, clinical, and deeply human.

The future of healthcare won’t always knock before entering.

We got into some personal territory today. The comments are open for analysis.

Thanks for reading DROIDS!! All of my newsletters and podcasts are free. Please encourage your friends to subscribe. It keeps the ideas flowing. Thanks!

Vocabulary Key

Urinalysis – Analysis of a urine sample to detect health issues

Anthropomorphic – Giving machines human-like traits

Biomarkers – Measurable indicators of health or disease

Digital medical record – A patient’s electronic health file

Uncanny valley – The eerie feeling when something looks almost human

Five FAQs

Is Urobot real? Yes. It’s being tested in a hospital in Taiwan.

What does it analyze? Flow rate, sugar, protein, pH, hydration markers, signs of infection.

Why the humanoid design? To comfort patients—though many find it unsettling.

Is the data private? Yes. Results go directly into hospital systems, not the cloud.

Will it be commercialized? Not yet. It’s still a clinical prototype.

#RobotToilet #UncannyValley #HealthTech #HumanoidRobots #SmartToilet #MedicalRobotics #DigitalHealth #AIHealthcare #FutureOfHealthcare #AmbientDiagnostics

Why it matters:

Most advanced robot AIs need the cloud to work—meaning they rely on internet connections to process instructions and make decisions. That’s a problem in places with poor connectivity or high-security requirements.

DeepMind’s new on-device model solves that by packing impressive vision-language-action skills (understanding what it sees and hears, and doing physical tasks) into a version small enough to run right on the robot.

What it can do:

* Understand verbal or visual instructions

* Manipulate objects it hasn’t seen before

* Learn new tasks with just 50–100 examples

* Run offline, in real time, and securely

Who it runs on:

* Initially trained on Google’s ALOHA robot

* Now running on Apptronik’s Apollo humanoid and Franka FR3 dual-arm robots

Why it’s a big deal:

This is a key step toward truly autonomous robots—ones that can operate independently, safely, and flexibly in the real world.

Learn more from Google Gemini Robotics.

Join the trusted user waitlist.

Additional Resources for Curious Humans:

The Verge"Google DeepMind’s optimized AI model runs directly on robots." The Verge, 24 June 2025.https://www.theverge.com/news/691882/google-deepmind-on-device-ai-robots-geminiDetails the launch of Gemini Robotics On-Device, its capabilities, and platform adaptations.

The Decoder"Google Deepmind unveils new AI models for robotic control." The Decoder, 12 Mar. 2025.https://the-decoder.com/google-deepmind-unveils-new-ai-models-for-robotic-control/Explains the vision-language-action and enhanced spatial models, partnerships, and real-world testing.

IndexBox"Google DeepMind Launches Gemini Robotics On-Device Language Model." IndexBox Blog, 24 June 2025.https://www.indexbox.io/blog/google-deepmind-unveils-gemini-robotics-on-device-model/Covers the model’s features, demonstrations, SDK release, and market context.

Bots.co.uk"Franka Research 3 Robot Arm." Bots.co.uk.https://bots.co.uk/cobots/franka-research-3/Technical specifications and research applications of the Franka FR3 robot.

Yahoo Finance"Google rolls out new Gemini model that can run on robots locally." Yahoo Finance, 24 June 2025.https://finance.yahoo.com/news/google-rolls-gemini-model-run-140000998.htmlReports on the model’s local operation, benchmarks, and industry context.

OODA Loop"Google’s new robotics AI can run without the cloud and still tie your shoes." OODA Loop, 24 June 2025.https://oodaloop.com/briefs/technology/googles-new-robotics-ai-can-run-without-the-cloud-and-still-tie-your-shoes/Focuses on autonomy, generalization, and the impact of generative AI in robotics.

TechCrunch"Google rolls out new Gemini model that can run on robots locally." TechCrunch, 24 June 2025.https://techcrunch.com/2025/06/24/google-rolls-out-new-gemini-model-that-can-run-on-robots-locally/Discusses the model’s local capabilities, demonstrations, and SDK release.

Google DeepMind Official Page"Gemini Robotics." Google DeepMind.https://deepmind.google/models/gemini-robotics/Official product page for Gemini Robotics On-Device and SDK.

The Times of India"Google launches Gemini Robotics, CEO Sundar Pichai says 'we’re taking our next step in...'" The Times of India, 12 Mar. 2025.https://timesofindia.indiatimes.com/technology/artificial-intelligence/google-launches-gemini-robotics-ceo-sundar-pichai-says-were-taking-our-next-step-in-/articleshow/118944188.cmsAnnounces the launch of Gemini Robotics and Gemini Robotics-ER, with CEO commentary and partnership details.

A robot springs from a Rivian van and strides up your driveway. It’s carrying a package and maybe… your future? Amazon’s latest humanoid experiment is a bold bet: that bipedal bots can tackle the most chaotic leg of delivery—the part between the curb and your porch.

Inside a modest-sized testing space in San Francisco—nicknamed the “humanoid park”—Amazon is training delivery robots to navigate obstacle courses designed to mimic suburban yards. Think hoses, stairs, and errant toys. The company is pairing its AI software with robot bodies from partners like Agility Robotics and Unitree. These bots may one day leap from electric Rivian vans to deliver packages while their human coworkers handle other stops.

This could be the next frontier in last-mile logistics. But don’t let the smooth stride fool you. Your driveway is a battlefield.

Delivery, Meet Dog

Ask any delivery worker about dogs, and you’ll hear stories worthy of epic poems. My cattle dog is convinced that every delivery is an attempted burglary. Now imagine that dog’s reaction to a metallic humanoid silently approaching the front door.

Humanoids, for all their processing power, don’t yet come with a canine-calming module. They’ll need to manage not just pets, but people, children, and the chaotic unpredictability of real homes.

The Obstacle Isn’t Always the Curb

Amazon’s bots train in a coffee-shop-sized test arena, but the real world is less forgiving. It’s not just stairs or slippery grass. It’s the coiled garden hose, the newspaper you forgot to pick up, the “present” left by a neighbor’s Great Dane. These aren’t props—they’re hazards.

Yet, the Amazon bots will also encounter the flip side—dogs so friendly and enthusiastic they will not leave them alone. There are dogs so thrilled to see delivery drivers that entire social media pages are devoted to them, showing heartwarming encounters where UPS drivers hand over biscuits to placid Golden Retrievers who’ve been waiting all day for their arrival like they’re a member of the family. The point? It is a case-by-scenario. Dogs are unpredictable.

Can these machines adapt on the fly? Amazon hopes reinforcement learning will help robots react to novel terrain. But roboticists agree: complexity scales fast when you leave the lab.

“If the environment is tightly controlled, like clear driveways and standard door layouts, it’s feasible,” says Prof. Subramanian Ramamoorthy of the University of Edinburgh. “Add pets, kids, or unexpected objects, and things get trickier.”

Why Bother With Bots?

Despite the messy reality, the prize is clear. Humanoid delivery robots could someday shave minutes off each drop-off by allowing drivers to stay in the vehicle. With more than 20,000 Rivian vans on the road and plans for 100,000, Amazon is building a fleet-ready future. Analysts estimate that humanoid delivery could save Amazon over $7 billion annually by 2032.

There’s also potential upside for workers. In Amazon warehouses, robots like Agility’s Digit allow human employees to shift into coordination roles—a kind of robot management. Bringing that synergy to streetside delivery might reduce injury risk and fatigue while keeping humans in supervisory positions.

Digit is well-suited for carrying payloads like packages, and is already working in factories.

Porch-Ready or Pipe Dream?

Amazon has already tried airborne delivery with drones and owns a self-driving vehicle firm, Zoox. This latest push is part of a grander vision: a fully robotic delivery pipeline.

Check out Digit climbing stairs like a champ in this image provided by Agility Robotics.

According to the Verge: The obstacle course reportedly contains one Rivian van for training purposes, with Amazon aiming to have humanoid robots “hitch a ride in the back of Amazon’s electric Rivian vans and spring out to deliver packages.”

Amazon is already using a variety of autonomous robots in its warehouse operations, including a trial of Agility Robotics’ humanoid “Digit”.

But, Digit isn’t the only robot being tested. The Information reports that a “variety” of humanoid robots will be tested for package delivery at its facility, including a $16,000 unit from China-based Unitree.

Am I the only one who can’t wait to see this in action? (Even though my dog will likely consider this a new form of metallic terror inflicted upon us? She still looks at our elderly mailman as some sort of mortal enemy.)

Still, one thing’s certain: the sidewalk is the next proving ground for AI on two legs.

Vocabulary Key

* Humanoid robot: A robot designed to mimic the human body, often with two legs, arms, and a head-like sensor array.

* Last-mile delivery: The final step in a product’s journey from warehouse to doorstep.

* Reinforcement learning: A type of machine learning where AI learns by trial and error, improving over time based on rewards or penalties.

* Digit: A bipedal robot from Agility Robotics used in warehouses and now tested for delivery roles.

* Rivian vans: Electric delivery vans developed by Rivian, used widely in Amazon’s logistics network.

FAQs

What is Amazon’s “humanoid park”? An indoor obstacle course for training delivery robots to handle real-world challenges like stairs, hoses, and tight spaces.

Are these robots fully autonomous? Not yet. They rely on AI for navigation but may initially operate under human supervision or in tandem with human drivers.

What kinds of robots is Amazon testing? Models include Agility Robotics’ Digit and units from Unitree, paired with Amazon-developed AI.

When will these robots start delivering packages? Amazon hasn’t given a timeline, but real-world field testing is planned after in-lab training concludes.

Will delivery workers lose their jobs? Not immediately. Amazon frames this as a support system—offloading repetitive tasks and creating supervisory roles.

For more on Digit, check out the video I did with the CTO, Pras Velagapudi, of Agility Robotics while at NVIDIA GTC.

Additional Reading for Curious Humans

AboutAmazon. Amazon announces 2 new ways it's using robots to assist employees and deliver for customers.

The Guardian: Amazon testing humanoid robots to deliver packages

The Verge: Amazon is reportedly training humanoid robots

The Information: Amazon prepares test of humanoid robot delivering packages

#HumanoidRobotics #LastMileDelivery #AmazonBots #AgilityRobotics #DigitTheRobot #AIOnTwoLegs #RobotVsDog #FrontYardEngineering #FutureOfLogistics #TheDroidsNewsletter

Tell us your thoughts on robots at your front door in the comments below!

At the bustling VegFest food festival in Santa Cruz, where the scent of sizzling jackfruit mingled with the chatter of kombucha fans, my son Alexander W. Torres —fresh from his robotics grad lab—froze mid-bite. His eyes had locked onto a squat, rugged machine parked nonchalantly beside an old green bus.

“That’s an ag bot,” he said, handing me his plate without a second thought. For Alex, lunch could wait—robots could not.

The machine stood under a white canopy marked “Farm-ng,” a local robotics outfit. Drawn in like a moth to a circuit board, Alex was soon deep in conversation with the team. Moments later, he was at the helm of their flagship platform—the Amiga—a modular, electric farm robot with the look of a sci-fi rover and the soul of a workhorse.

“It runs on NVIDIA Jetson,” explained the rep from Farm-ng. I leaned in closer to hear more.”Leave them be,” my husband whispered to me. “Let your son enjoy the robot.”

Meet the Amiga: Agriculture’s Adaptive Ally

The Amiga isn’t your typical farm equipment. Weighing just 320 pounds and priced under $13,000, this modular, electric robot is designed for versatility. Its capabilities range from soil preparation and planting to harvesting and data collection. The robot’s adaptability allows it to navigate diverse terrains and perform tasks tailored to specific crops and farm needs.

What sets the Amiga apart is its open architecture. Farmers, developers, and researchers can customize its functions using the Amiga Development Kit (ADK), fostering innovation and collaboration in the agricultural community.

Empowering Small and Medium-Sized Farms

In an industry dominated by large-scale operations, small and medium-sized farms often struggle with labor shortages and high operational costs. Farm-ng aims to level the playing field by providing affordable automation solutions. The Amiga’s modular design and user-friendly interface make it accessible to farmers without extensive technical backgrounds.

By automating repetitive and labor-intensive tasks, the Amiga helps reduce reliance on manual labor, allowing farmers to focus on more strategic aspects of their operations. This shift not only improves efficiency but also contributes to the sustainability and resilience of smaller farms.

Cultivating the Next Generation of AgTech Innovators

Farm-ng’s commitment to education and innovation extends beyond the fields. Through initiatives like the University Farm Robotics Challenge, the company collaborates with academic institutions to inspire and train future agricultural technologists.

Students and researchers are encouraged to experiment with the Amiga platform, developing new applications and solutions to address real-world farming challenges. This hands-on experience not only enhances learning but also accelerates the development of cutting-edge agricultural technologies.

A Glimpse into Agriculture’s Future

The encounter at VegFest was more than a curious detour—it was a glimpse into how robotics is quietly transforming one of humanity’s oldest industries. As machines like the Amiga roll into orchards and greenhouses, they signal a future where automation isn’t a threat, but a tool—precision-tuned for farmers navigating labor shortages, climate challenges, and rising costs.

For Alex, it wasn’t about crops—it was about code, control systems, and the quiet thrill of watching a robot respond to real-world complexity. And for a generation of roboticists like him, agriculture may be one of the most unexpectedly fertile testing grounds. Where better to teach a machine to adapt than a field that never stops growing?

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🎙️ Podcast Transparency: The voices in this episode are AI-generated using Google DeepMind NotebookLM. This podcast was created from the original interview transcript with Farm-ng staff at VegFest, this article, the Farm-ng website, the AgFunderNews article on Farm-ng’s Series A, the CBS News segment on AI-powered farm robots, and the TechCrunch article on modular ag bots.

Additional Reading for Curious Humans

* Farm-ng Official Website

* TechCrunch: Farm-ng’s Modular Robots

* CBS News: AI-Powered Robots in Agriculture

* AgFunderNews: Farm-ng’s $10M Series A Funding

* Farm Robotics Challenge

Vocabulary Key

* Modular Design: A system architecture that allows components to be independently created and then used in different systems.

* Open Architecture: A type of computer architecture or software architecture intended to make adding, upgrading, and swapping components easy.

* Automation: The use of technology to perform tasks without human intervention.

* AgTech: A sector that combines agriculture and technology to improve farming practices.

* Sustainability: Practices that meet current needs without compromising the ability of future generations to meet theirs.

FAQs

What is Farm-ng? Farm-ng is a robotics company based in Watsonville, California, specializing in developing modular, electric robots for agricultural applications.

What tasks can the Amiga perform? The Amiga can handle various farming tasks, including soil preparation, planting, harvesting, and data collection, and can be customized for specific needs.

Who can use the Amiga? The Amiga is designed for small to medium-sized farms, researchers, and developers interested in agricultural automation.

How does the Amiga support education? Through programs like the University Farm Robotics Challenge, Farm-ng provides platforms for students and researchers to develop and test agricultural technologies.

Where can I learn more about Farm-ng and the Amiga? Visit Farm-ng’s official website for more information on their products and initiatives.

Note: Alexander W. Torres will be joining the DROIDS Newsletter team this summer as a Writer and Editor.

#AgRobotics #FarmTech #ModularRobots #FarmNG #AmigaBot #FutureOfFarming #RoboticsInTheWild #AutomationNation #RobotSpotting #VegFest2025 #DROIDsNewsletter #NextGenEngineering #STEMinTheField #NVIDIA #NVIDIAJetson

The parking lot was packed. We know it well—it’s the same plaza as Illusive Comics, where we’ve suited up with the 501st Legion on Free Comic Book Days. A friendly security guard waved us in and handed us each a collectible mug. It wasn’t even noon and the nerd joy was already brewing.

Inside? Aisles of GPUs, CPUs, SSDs, and peripherals, all glistening under different shades of neon showroom lights.

“I never need to go to Best Buy again,” someone muttered behind us.

And then we saw it—a golden NVIDIA GPU signed by Jensen Huang, displayed behind glass like the tech world’s Crown Jewel. We both stopped and just studied it— like it was a museum piece.

Giveaways continued—with 128GB flash drives for the devoted fans who could show the right QR code. Even the staff radiated startup energy, helping customers navigate the maze of motherboards and cooling systems like it was their personal mission.

A Long-Awaited Return

Micro Center’s reopening in the Bay Area has been the stuff of legend among local techies. The original Santa Clara store closed in 2012 due to a landlord dispute, leaving a void in the heart of Silicon Valley—a paradox, really, for the world’s innovation hub to lack its own hands-on tech emporium.

Micro Center makes technology tactile again. In a world increasingly virtual, this matters.

“Hey, this is the same sensor I used on my senior capstone project,” my son said, checking out all the components and already imagining a future build. He explored further, picking through aisle after aisle. “I’ll need to learn to solder, but there’s all the parts I would need here to build a robot arm.”

The Legendary FilaMonster Wall

One of the standout features at the new Micro Center Santa Clara is the impressive “FilaMonster”—a massive, floor-to-ceiling wall dedicated entirely to 3D printing filament. Aptly nicknamed by the community, the FilaMonster showcases an incredible spectrum of filament types, colors, and brands, making it a visual centerpiece and a must-see for any maker or 3D printing enthusiast.

When I worked as a STEM Lab instructor for the local school district, my son would come in to help run the 3D printing labs. When the world shut down for COVID, we organized local makers to print face shields for front line responders. My son printed over 2,000 of them and we hand-delivered them to local hospitals and doctors. Suffice it to say, 3D printing is a common shared passion.

Walking up to the FilaMonster feels like stepping into a candy store for creators. Whether you’re searching for classic PLA, specialty PETG, flexible TPU, or exotic blends, the wall is stocked with spools in every hue imaginable. But, how you access the filaments is what makes the “Monster” so unique.You flip through samples— see the photo and video above— in a manner just like checking out paint colors at your local Home Depot. Once you select a color and material, you bring the “chip” over to the FilaMonster wall. The wall does the rest— and remember to deposit your “chip” back in the Minecraft cube. It’s all very entertaining.

Find It Next to the Comic Book Store

Opening day is tomorrow.

Opening Day: Friday, May 30, 2025

Location: 5201 Stevens Creek Blvd, Santa Clara, CA

And yes, it’s right next to the comic book store, so you can grab a new GPU and the latest Batman/Deadpool crossover—because why not max out both your frame rate and your multiverse.

📚Additional Reading for Curious Humans

* Micro Center Santa Clara Grand Opening

* Reddit: Early Access Day Reflections

* PCMag: Inside the New Store

* Influencer Schedule for Micro Center Opening

🔍 FAQs

What makes Micro Center unique for AI enthusiasts? Hands-on access to hardware like GPUs that power deep learning models.

What’s the emotional draw of Micro Center? It’s where digital dreams meet physical reality—for families, creators, and pros.

Will this affect the Bay Area tech scene? Absolutely—expect more local events, hardware meetups, and maker culture.

What’s the opening date? Friday, May 30, 2025.

Why write this piece? To capture the blend of nostalgia and innovation—and because we’re nerdest. The coolest kind of nerds. But, yeah, nerds.

And, if you make it in time to get the free merch, this was the mug giveaway… (We did not wait on line for the free thumb drive. Line was too long.)

Alexander Wolf Torres will be joining the DROIDS Newsletter Staff as an Editor and Writer this summer.

#deeplearning #aihardware #microcenter #microcentersantaclara #nerdculture #gpus #robotics #comicbooks #batmandeadpool #techhumor #siliconvalley #droids #thedroidsnewsletter

A small, wide-eyed robot closes its eyes. A glowing bubble floats above its head, filled with images of laundry, soft toys, and a robotic arm folding shirts in a sunlit bedroom. This isn't a Pixar short. It's a test project built using Google Veo, inspired by a very real breakthrough in robotic learning: NVIDIA's new DreamGen framework. (To clarify, I read a paper by Jim Fan about robots who dream of folding laundry and decided to try out Google Veo both in the same day. Hence, today, you have Google Veo videos of robots folding laundry while I talk about the Jim Fan paper. I’d like to clarify that these images were generated by me- and were in no way generated by NVIDIA.)

DreamGen is what happens when robotics meets imagination. Or more precisely, when foundation video models simulate millions of "what if" scenarios, giving robots access to experiences they’ve never actually lived. It's a kind of synthetic dreaming, and it just might redefine how machines learn physical tasks. As I discovered when I tried various prompts of robots folding laundry, I had a lot more failures than successes.

The Paper: Introducing DreamGen

Unveiled by Jim Fan and the NVIDIA GEAR team, DreamGen proposes a four-step recipe:

* Fine-tune a SOTA (state-of-the-art) video model on your target robot.

* Prompt it with language, asking how your robot would behave in imagined scenarios.

* Recover pseudo-actions from those videos using inverse dynamics or latent action modeling.

* Train a robot foundation model on this hallucination-turned-data, as if the robot had experienced it.

And the results? DreamGen took a robot trained only on a single task—pick-and-place—and enabled it to generalize to 22 new behaviors. That includes pouring, scooping, ironing, and hammering. With no human demos. No teleoperation. Just dreams.

Why It Matters

This is more than clever data augmentation. It's a shift in how we think about experience itself. Robots trained this way can generalize to unseen verbs and objects. In internal tests, success rates jumped from zero to over 43% on new verbs. For unseen environments, performance went from 0% to 28%.

In other words: the robot imagined folding a towel—then actually did it.

The Veo Test: A Dream of Laundry

Curious how this might look from the robot’s perspective, I turned to Google Veo and Flow. Still early days in terms of interface polish, but the potential is unmistakable.

I started with a simple prompt: a robot tucked in bed, dreaming about folding laundry. The results were mixed. Some of my earliest attempts spun into dystopian robot nightmares. Yikes! What was this? Is this robot pregnant and spawning another robot?

And, in my second attempt, the robots rebelled against their overlords and started to shred the laundry. Don’t laugh. This is how the robot rebellion will happen.

But then came my first “wins”— the videos I included up above where the robot— watched over by his faithful pal teddy bear- learns to fold towels.

It’s not technically DreamGen—but it captures the same core idea in a way that’s instantly understandable. Watching a robot dream about folding laundry is a visual stand-in for something much bigger: the ability to learn from imagined experience.

DreamGen is doing something profound. It’s giving robots a way to simulate tasks they’ve never been shown, to fill in the gaps with plausible futures, and to act on that learning. That’s hard to explain in code—but it’s easy to see in a dream sequence.

This little short isn’t just cute. It makes the concept real. You watch it, and you get it: the robot didn’t have to be taught everything. It imagined the task first.

The Takeaway

DreamGen is part of a bigger shift in robotics and AI: toward training paradigms that prioritize imagination, simulation, and low-cost data generation over fleets of human operators. It’s also deeply visual—making it the perfect muse for tools like Google Veo.

And if you happen to generate a robot dream sequence that includes the robot folding itself into a drawer and humming a lullaby? Just go with it. These models are dreaming, too.

The podcast was generated using NotebookLM and reviewed for accuracy.

🎙️ PODCAST NOTE:

If you’re wondering what “GEAR” means, don’t worry, I had to look it up too. GEAR stands for Generalist Embodied Agent Research, and it’s a division within NVIDIA Research focused on building robots that can generalize, meaning they don’t just memorize tasks; they adapt to new ones. So when the male AI podcasters say “GEAR Research,” he is referring to the folks at NVIDIA working on some of the most advanced robotic learning systems out there.

Additional Resources For Curious Humans:

* DreamGen Research Paper

* Jim Fan on X

* Google Veo Overview

#AI #Robotics #DreamGen #RobotLearning #TextToVideo #FoundationModels #NVIDIA #PhysicalAI #SyntheticData #MachineLearning #RobotDreams #DROIDS #JimFan #DreamGenpaper #thedroidsnewsletter

While headlines fixate on the form—yet another biped in a growing crowd of humanoid contenders—Beyond Imagination’s real play is upstairs. Beomni’s AI “brain” isn’t just coded intelligence; it’s designed to think, adapt, and perform complex tasks with a level of generality that echoes the ambitions of AGI itself.

Unlike single-purpose factory bots, Beomni’s architecture includes real-time environmental sensing, object recognition, haptic communication, and task planning that lets it not just work—but learn. In collaboration with Carnegie Mellon, the system observes human behavior in virtual reality to refine its capabilities. This makes Beomni one of the first truly teleautonomous humanoids: it can be piloted, but it’s learning to think independently.

Aura: The Invisible Operating System

Co-founder Harry Kloor calls it "a bridge between humans, robots, and legacy machines." Aura, Beyond Imagination’s universal operating system, is arguably more revolutionary than the robot itself. It’s designed to coordinate complex tasks across humans and machines, serving as the invisible nervous system of an intelligent manufacturing floor—or hospital, or logistics hub.

In an industrial world crammed with outdated systems and siloed hardware, Aura aims to unify. Think Windows, but for a hybrid workforce of people and bots.

Kurzweil’s Long Game

Behind the machine is a philosophy. Ray Kurzweil, the legendary AI futurist who now serves as Chief AI Officer, has long predicted the convergence of human and machine intelligence. Beomni isn’t just a product; it’s a prototype for his vision of a post-scarcity world where labor is decoupled from survival.

Kurzweil’s approach departs from the arms-race pace of Chinese robotics firms, many of which are focused on form factors and manufacturing efficiency. Beyond Imagination is trying to make something different: an embodied AI that can grow across domains. A workforce of adaptable intelligences that learn as they go, teach one another, and evolve.

Yes, There’s a Robot Too

Beomni, the humanoid form carrying this all, looks less like a person and more like the concept of utility sculpted into chrome. It’s sleek, headless, and designed for precision tasks in pharmaceuticals, semiconductor labs, and high-risk industrial zones. But its most radical feature isn’t its frame—it’s what’s happening inside.

From Silicon to Soul

Beyond Imagination’s new $100 million investment from Gauntlet Ventures values the company at half a billion dollars. But its true worth may lie not in humanoid hardware, but in Kurzweil’s bid to redefine labor itself.

Rather than flood the world with affordable bots, Beyond Imagination is planting a seed. One that grows brains before bodies—and might one day change how we think about work, learning, and what machines are for.

Vocabulary Key

* Teleautonomous: Capable of remote control and independent decision-making.

* Haptic Communication: Technology that simulates touch through feedback systems.

* Legacy Machines: Older industrial systems still in use, often without modern connectivity.

* AGI (Artificial General Intelligence): AI with the ability to understand or learn any intellectual task a human can.

* Post-Scarcity: A hypothetical economy in which most goods can be produced in great abundance with minimal human labor.

FAQs

What is Beyond Imagination building? A humanoid robot (Beomni), an AI system that can learn and adapt, and Aura—a universal OS for robots and humans.

Why focus on the AI and OS over the robot body? Because true progress in robotics depends on intelligence and integration, not just hardware.

What makes Beomni different from other humanoids? Its combination of learning AI, remote operation, and real-world general task application.

What industries is Beyond Imagination targeting? Pharmaceuticals, semiconductors, logistics, healthcare—any field needing skilled, adaptable labor.

How does Ray Kurzweil’s vision shape this company? Kurzweil’s long-term view aims at intelligent systems that evolve over time and help restructure the economy around abundance.

Will it reach a point where we can no longer decide which robot to buy?

Additional Reading for Inquisitive Humans:

* “Beyond Imagination raises $100 million to build humanoid robots” – Fast Company

* Renowned futurist Ray Kurzweil reportedly raising $100 million to build humanoid robots. Robotics and Automation News.

* Exclusive: Ray Kurzweil's humanoid robot startup in talks for $100 million investment. Reuters.#HumanoidRobots #AIInnovation #FutureOfWork #RayKurzweil #IndustrialAutomation

Building Sweden’s AI Superpower

At the heart of this venture lies the deployment of two NVIDIA DGX SuperPODs, powered by the cutting-edge Grace Blackwell GB300 systems. This infrastructure is set to become Sweden’s largest enterprise AI supercomputer, offering secure and sovereign compute access to consortium members. The system is designed to handle compute-intensive AI workloads, facilitating the training of domain-specific models and large-scale inference tasks, including reasoning AI .

To bolster this infrastructure, NVIDIA plans to inaugurate its first AI Technology Center in Sweden. This center will serve as a hub for joint research and upskilling, providing access to NVIDIA’s experts and hands-on training through its Deep Learning Institute.

Industry-Specific Applications

Each consortium member brings a unique perspective to the AI factory:

* AstraZeneca: Aims to leverage the supercomputer for AI-enabled drug discovery and development, utilizing methods like foundation model training and multi-modal inference.

* Ericsson: Plans to enhance performance and efficiency in telecommunications by developing state-of-the-art AI models, thereby improving customer experiences.

* Saab: Intends to accelerate the development of advanced defense capabilities by integrating AI methodologies with its existing platforms.

* SEB: Seeks to drive productivity enhancements and future-proof banking services by embedding AI into its operations, ensuring secure access to critical infrastructure.

Strategic Vision and National Impact

Marcus Wallenberg, Chair of Wallenberg Investments, emphasized the strategic importance of this initiative:

“Investing in cutting-edge AI infrastructure is a crucial step toward accelerating the development and adoption of AI across Swedish industry. We believe this initiative will generate valuable spillover effects—by enabling upskilling, fostering new collaborations, and strengthening the broader national AI ecosystem.”

NVIDIA’s CEO, Jensen Huang, highlighted the transformative potential of AI:

“As electricity powered the industrial age and the Internet fueled the digital age, AI is the engine of the next industrial revolution. Through the visionary initiative of Wallenberg Investments and Sweden’s industry leaders, the country is building its first AI infrastructure—laying the foundation for breakthroughs across science, industry, and society.”

Ethical Considerations and Future Outlook

While the AI factory promises significant advancements, it also raises important ethical considerations. Ensuring data privacy, algorithmic transparency, and equitable access to AI technologies will be paramount. The consortium’s commitment to sovereign and secure compute access is a step toward addressing these concerns.

This initiative positions Sweden as a potential leader in responsible AI development, setting a precedent for how nations can collaboratively build and manage AI infrastructure. As the project unfolds, it will be crucial to monitor its impact on innovation, employment, and societal well-being.

Thanks for reading DROIDS!! Subscribe for free to receive new posts and support my work.

Parts List: Key Terms

* DGX SuperPOD: A scalable AI supercomputing infrastructure developed by NVIDIA, designed for training large AI models.

* Grace Blackwell GB300: NVIDIA’s latest AI processor architecture, optimized for high-performance computing tasks.

* Sovereign Compute Access: Ensuring that computing resources are controlled and managed within a nation’s jurisdiction, enhancing data security and autonomy.

Systems Check: What You Need To Know

Q1: What is the primary goal of the AI factory in Sweden?

To build the country’s largest enterprise AI supercomputer, facilitating advanced AI research and applications across various industries.

Q2: Who are the key partners in this initiative?

AstraZeneca, Ericsson, Saab, SEB, Wallenberg Investments, and NVIDIA.

Q3: How will this project impact Sweden’s industries?

It aims to accelerate AI adoption, enhance productivity, and foster innovation in sectors like healthcare, telecommunications, defense, and finance.

Q4: What measures are being taken to address ethical concerns?

The consortium emphasizes sovereign and secure compute access, aiming to uphold data privacy and algorithmic transparency.

Q5: When is the AI factory expected to become operational?

While specific timelines haven’t been disclosed, the first phase involves deploying two NVIDIA DGX SuperPODs, indicating a significant progression in the near future.

Additional Reading for Curious Droids:

* Wallenberg Investments Press Release

* SEB’s Announcement on Joining the Consortium

* Sweden Herald’s Coverage on the AI Supercomputer

#ai #supercomputing #robotics #humanoidrobots #sweden #nvidia #futureofwork #infrastructure #technology #deeplearning

In that moment, I’m reminded why we’ve kept our Teslas. Service that comes to you, without upsells or stress. It’s seamless, personal, and quietly revolutionary.

And then—like a software update that erases your favorite feature—I remember who runs the company. The moment feels surreal: a service so flawless and human-centered, contrasted with leadership that often feels erratic and out of touch.

The Service Is Still Revolutionary

Tesla’s mobile service is one of the company’s most underrated innovations. According to Tesla’s 2023 Impact Report, over 30% of its North American service visits now happen remotely. I’d heard about this “perk” before we purchased our first Tesla, but never looked into it. If I had, I probably would have purchased an EV years earlier for this one benefit alone. Beyond the extraordinary luxury of car service in your driveway, EVs require less service. (Unless you own a Cybertruck and you’ve had been tortured through eight recalls in six months. But, we won’t go there.)

EV Maintenance: Fewer Parts, Fewer Problems

Electric vehicles have a mechanical simplicity that dramatically reduces service needs. Most EV drivetrains contain just 20–25 moving parts, compared to 200–2,000 in an internal combustion engine. That translates into fewer failure points, no oil changes, and simplified transmissions. Regenerative braking also reduces wear on brake pads, extending the lifespan of the braking system. And without tailpipes, mufflers, catalytic converters, or oxygen sensors, there’s no exhaust system to service or replace.

The U.S. Department of Energy confirms that EVs “typically require less maintenance than conventional vehicles,” while multiple industry analyses estimate a 40% to 49% reduction in service costs compared to gas-powered cars. Ford, for example, reports that scheduled maintenance for its F-150 Lightning is 49% cheaper than for the combustion-powered F-150 over five years or 75,000 miles.

(Full disclosure: I have serious concerns about the public safety impact of turning massive pickups into even heavier battery-electric vehicles. While some call the F-150 Lightning a “marvel of engineering,” I nervously see it as a 6,000-pound battering ram in traffic. That said—Ford isn’t wrong about the maintenance savings.)

While EVs still require tire rotations, cabin air filter changes, and the occasional software or coolant update, the overall cost of ownership remains significantly lower—often offsetting the higher sticker price within a few years.

These design advantages eliminate entire service categories—spark plugs, mufflers, timing belts—and cut down on shop visits. Paired with Tesla’s remote diagnostics and over-the-air software updates, EV ownership becomes not just cheaper, but far more convenient.

How much does it cost for all this driveway convenience? Our last service cost $65 per car. We’ve spent more than that on vegan burgers and drinks. On a scale of one to fun, watching the Tesla service tech work out our [home] office windows never gets old.

People Are Ditching Their Teslas Like The Titanic Headed For an Iceberg

Yes, some celebrities have jumped ship:

* Bette Midler: Called her Tesla “a symbol of racism, greed and ignorance.” (Perhaps I should have used the analogy “the Hinderberg coming in for an ill-fated docking maneuver.”)

* Sheryl Crow: Sold hers and donated the money to NPR.

* Jason Bateman and Senator Mark Kelly also distanced themselves over Musk’s politics.

(Sources)

From Road King to Roadkill: Tesla’s Historic Sales Collapse

The impact on Tesla sales in 2025 hasn’t just been sharp—it’s been historic. Global vehicle deliveries fell 13% year-over-year in Q1, down from 495,570 in Q4 2024 to just 336,681. That’s a staggering 32% quarter-over-quarter drop—the steepest in the company’s history.

The Wheels Came Off: Tesla Sales Tank Globally

Europe’s numbers are even more dire. Sales are down nearly 40% year-to-date and collapsed 46% in April alone. Model Y sales dropped 51% that month. Country by country: the UK is down 62%, Denmark 67%, Netherlands 74%, Sweden 81%, France 59%, and Portugal 33%. In Quebec, Tesla sales plummeted 90% in Q1.

Too Fast, Too Furious, Too Elon: The Tesla Retraction Begins

This isn’t a macro EV problem—EV sales are up 11% in the U.S. during the same period. Tesla’s downturn is driven by backlash against Elon Musk’s politics, growing competition from Chinese automakers like BYD (which now outsells Tesla in Europe), and a stagnating product line. Analysts project a further 5–9% global decline by year’s end. Tesla’s Q1 net income fell 71%.

But Owners Aren’t Letting Go

Despite a significant reputational hit and increased trade-ins amid the Elon Musk backlash, Tesla owners display the highest loyalty and retention rates in the automotive industry. The data shows that while some owners leave, the vast majority are sticking with their Teslas, and most say they would buy another. Tesla’s product experience, charging network, and EV leadership continue to outweigh the CEO’s controversies for most owners. Despite increased competition and negative headlines, Tesla’s owner loyalty and retention remain industry-leading in 2025, and the brand continues to outperform all rivals on this metric. And I see it too: On my morning walk, it’s not uncommon to see multiple Teslas in a row drive past. On the freeway, I still end up in convoys of Teslas. (Somehow, the cars seem to prefer traveling in packs. Go figure.) And, when I park my car, I come out and it’s made a friend. There is often another Tesla parked next to it.

The Teslas I see are primarily the “classic” ones. Not the new Cybertruck aesthetic ones. The ones that whisper, “I bought in when this felt like the future.”

But for me? I won’t buy another. Not as long as the company is led by someone whose values I find irreconcilable. Trust matters—and I wouldn’t trust this CEO to make me a cup of coffee, let alone guide company policy. (OMG- just picturing that man trying to make coffee. LOL.)If they fire his racist butt? Yeah, I’d consider getting another one.

The Ethical Reckoning

My internal conflict isn’t about hardware. It’s about humanity.

In 2025, Bill Gates publicly accused Elon Musk of “killing the world’s poorest children”—not figuratively, but directly—through his role in overseeing catastrophic cuts to the U.S. Agency for International Development (USAID) under the Trump administration. As head of the so-called Department of Government Efficiency (DOGE), Musk advocated slashing global aid by nearly 80%. These weren’t abstract budget lines—they were maternal care clinics, HIV prevention programs, and child nutrition efforts in some of the most fragile parts of the world.

Gates cited the abrupt termination of grants to hospitals in Mozambique that were preventing mother-to-child HIV transmission, calling it one of the worst reversals of public health progress in decades. UNICEF, the WHO, and other agencies echoed the alarm: millions of women and children are now cut off from lifesaving care. A KFF analysis found that 86% of USAID’s maternal and child health awards were terminated. UNICEF projects 14 million children will face nutrition disruptions this year alone, with modeling studies predicting hundreds of thousands of preventable deaths.

Elon Musk’s response? He compared the cuts to “feeding USAID into the wood chipper.”

Inside the Tesla: logic, clarity, renewable energy.

Outside: a figurehead destabilizing public health, mocking humanitarian norms, and tweeting conspiracy theories like it’s performance art.

The contrast could not be more jarring.

Reclaiming the Machine

This machine was never his.

It was built by engineers, designers, and quiet geniuses with socket wrenches and source code.

It was shaped by vision, not vanity.

Because I’m not mad at the car.

I’m mad at what it’s been forced to represent.

Tesla can be more than one man’s echo chamber.

It always was. It meant something to the people who drove it. Still does.

We—what they once called Tesla Nation—were always more than Elon Musk.

And we’ll still be here long after he’s replaced by someone who understands the base.

Someone who leads with intelligence, not impulse.

Someone who knows that trust, once lost, doesn’t reboot with a software update.

Thanks for reading DROIDS!! Subscribe for free to receive new posts and support my work.

Check out the video I did when we first bought the car. Things felt different back in mid- October 2022.

My favorite snarky comment on the video:

Other articles on Tesla by Deep Learning:

Spotify: Deep Learning with the Wolf. Tesla's Troubled Times (February 14, 2025.) What is Tesla’s end-to-end neural network? (April 1, 2024.) LinkedIn.[First-hand accounts] FSD Beta 12 and Tesla's "Meet the Engineers" Event. (March 31, 2024. LinkedIn.

Does Buying the SLATE mean we've given up on TESLA?(April 25, 2025.) LinkedIn.

#Tesla #EV #ElonMusk #TechEthics #ElectricVehicles #FSD #AIAccountability #EngineeringMatters #EVMarket #ReclaimTheMachine #DeepLearningwiththeWolf #theDroidsNewsletter #TeslaModel3 #TeslaNation #TeslaAutoPilot #TeslaFSD

The Gender Gap in Domestic Labor

Globally, women still shoulder the majority of unpaid domestic work, from cleaning to caregiving. The World Economic Forum estimates that, at the current pace, true gender parity is over a century away. The COVID-19 pandemic further exposed how domestic duties can push women out of the workforce, underscoring the urgent need for solutions that ease these burdens.

Robotics: Leveling the Playing Field?

Ohanian envisions a near future where robotics handle a significant share of household chores, freeing up time for both parents and making it easier for men to participate in caregiving. Studies suggest that by the 2030s, robots could automate up to 39% of domestic tasks, with grocery shopping and cleaning among the most easily automated. This could give families-especially women-more time for paid work, leisure, or rest.

Caveats and Social Implications

While the promise is real, experts caution that technology alone won’t erase deep-seated social norms. Past advances, like washing machines, didn’t reduce women’s unpaid labor as much as expected; instead, standards for cleanliness and parenting simply rose. Without cultural change, AI and robotics risk reinforcing, rather than eliminating, gendered expectations.

The Road Ahead

Ohanian’s optimism is well-founded: robotics could be a catalyst for change, offering practical help and sparking conversations about equality at home. But for true progress, society must pair technological innovation with shifts in attitudes and policies that support shared responsibility in caregiving and domestic work.

Bottom Line:Robotics may soon help balance the scales at home, but closing the gender gap will require more than just smart machines-it will take a collective rethink of roles, expectations, and support systems.

Citations:

* https://news.yahoo.com/tech/science/articles/alexis-ohanian-says-robotics-soon-022403076.html

* https://www.weforum.org/stories/2023/04/ai-housework-gender-gap-robots/

* https://pmc.ncbi.nlm.nih.gov/articles/PMC8020072/

* https://milkeninstitute.org/events/global-conference-2025/speakers/alexis-ohanian

* https://theconversation.com/the-dawn-of-domestic-robots-could-dramatically-cut-gender-inequality-when-it-comes-to-household-work-205778

* https://www.businessinsider.com/tech

* https://www.ox.ac.uk/news/features/ai-automation-home-and-its-impact-women

* https://techpolicy.press/ai-solutions-for-domestic-labor-may-exacerbate-inequities

* https://iconnections.io/global-alts-miami-speakers-and-agenda/global-alts-speakers/

* https://techxplore.com/news/2024-11-men-women-robot-age.html

* https://peopleandrobots.wisc.edu/wp-content/uploads/sites/1469/2020/12/IDC2020-CagiltayB.HoH_.R.MichaelisJ.E.MutluB..pdf

* https://www.forbes.com/sites/adigaskell/2022/01/14/how-robots-could-lower-the-gender-pay-gap/

* https://technologyandsociety.org/taking-care-with-caregiving-robots/

* https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1346257/full

* https://iwpr.org/women-automation-and-the-future-of-work-executive-summary/

* https://www.oii.ox.ac.uk/news-events/men-more-willing-to-be-cared-for-by-robots-in-their-old-age-than-women-finds-new-oxford-study/

* https://www.techtimes.com/articles/310243/20250507/alexis-ohanian-says-robots-will-handle-your-chores-so-parents-can-finally-breathe.htm

* https://www.womentech.net/how-to/are-women-key-ethical-developments-in-robotics

* https://pmc.ncbi.nlm.nih.gov/articles/PMC11267593/

* https://www.reddit.com/r/Futurology/comments/1grbxpd/men_more_willing_than_women_to_accept_robot_care/

* https://www.science.org/doi/10.1126/scirobotics.adk3307

* https://milkeninstitute.org/events/global-conference-2025/speakers

* https://soheylenglish.com/wp-content/uploads/2021/12/Forbes-Africa.pdf

* https://397news.com/publications/16830036004741/050223.pdf

* https://www.globalplayer.com/podcasts/42KqDz/

* https://milkeninstitute.org/events/global-conference-2022/speakers

* https://genderedinnovations.stanford.edu/case-studies/genderingsocialrobots.html

* https://pmc.ncbi.nlm.nih.gov/articles/PMC9363362/

* https://www.sciencedirect.com/science/article/abs/pii/S1043951X22001109

#robotics #droids #thedroidsnewsletter #thedroidnewsletter #sciencedirect #milkeninstitute #science #peopleandrobotics #techpolicy #domesticrobots #AlexisOhanian #reddit #redditfounder #robotsinthehome #homerobots #genderequality #domesticlabor #caregiving #worldeconomicforum

Unlike the autonomous enforcers of science fiction, Thailand's AI Police Cyborg serves as a stationary surveillance powerhouse. Equipped with an array of advanced features—360-degree smart cameras, facial recognition software, and sophisticated behavior analysis tools—the robot functions as a high-tech observation tower. By processing live feeds from both CCTV networks and airborne drones, it can simultaneously monitor large crowds, identify potential security risks, and even distinguish between genuine weapons and the playful water guns ubiquitous during Songkran celebrations.

While currently constrained to a wheeled platform rather than walking beats alongside human officers, the robot's impact on public safety is already tangible. Acting as an AI-enhanced "eye in the sky," it processes thousands of data points in real time, alerting human officers to potential threats with unprecedented speed and precision.

The deployment has ignited important discussions about the future of AI in law enforcement. Privacy advocates, security experts, and civil liberties groups have raised valid concerns about facial recognition accuracy, potential bias in AI systems, and the broader implications of automated surveillance in public spaces. These debates underscore the delicate balance between technological advancement and individual rights—a balance that Thailand and nations worldwide must carefully navigate as similar systems become more prevalent.

As one technology observer noted, this development signals the beginning of a new paradigm where silicon assistants work alongside human judgment to maintain public order. While not yet a roving RoboCop, Thailand's AI Police Cyborg marks a significant milestone in the evolution of law enforcement technology—one that promises enhanced security while challenging us to consider the ethical boundaries of automated authority.

As this technology matures, the global law enforcement community will be watching closely to see how Thailand's experiment in AI-assisted policing develops, setting precedents for similar deployments worldwide.

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#AIEthics #SurveillanceState #PrivacyMatters #RoboCop #BlackMirrorIRL #BigBrother #AIPolicing #FacialRecognition #ThailandTech #CyberSecurity #FutureOfPolicing #DigitalRights #TechDystopia #AIOverreach #SurveillanceCapitalism

But the phrase stuck.

Sort of.

These days, the word "physical" has less to do with spandex and more to do with servos, sensors, and synthetic muscle.

Welcome to Physical AI—the branch of artificial intelligence focused on helping machines operate in the real world. If traditional AI lives in servers and chatbots, Physical AI lives in robots, drones, autonomous vehicles, and other machines that interact with the messy unpredictability of gravity, friction, and humans who occasionally walk right in front of them.

In this post, I’ll break down:

* What Physical AI actually means

* Why NVIDIA, Disney, and just about every robotics company is talking about it

* How simulation, reinforcement learning, and real-world robotics are connected

* And yes, why it's the hottest trend in tech that doesn’t involve Lululemon

I’ve written about Physical AI before in Deep Learning with the Wolf. Check it out.

I also made my own short explainer video about Physical AI.I

So What Is Physical AI?

When most people hear “AI,” they think of large language models, chatbots, or generative art tools. These systems live in the cloud and operate purely in the digital world.

Physical AI is different—it’s about helping machines function in the physical world.

That means understanding:

* Friction

* Balance

* Motion planning

* How to not fall down a flight of stairs or crush your coffee table

As NVIDIA puts it: “Physical AI combines perception, reasoning, and action in machines that operate in the real world.”

This is the domain of robots, self-driving cars, drones, and sim-to-real learning. These systems must not only think—but move.

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Part 2: Why Does This Matter?

Because real-world robots aren’t just executing code. They’re reacting to environments that shift by the second. Gravel paths. Uneven sidewalks. People suddenly doing TikTok dances in front of them.

Physical AI gives machines the capacity to:

* See (through computer vision)

* Think (via onboard AI + edge computing)

* Act (via motion control, balance systems, etc.)

* And crucially: adapt on the fly

Part 3: From Sim to Sidewalk

Training these bots can be risky and expensive—so NVIDIA’s strategy starts in simulation.

Using tools like Isaac Sim, robots can learn to walk, navigate obstacles, or even show social behaviors—entirely in a virtual environment. This is called sim-to-real learning. And once they’ve learned in sim, those behaviors get transferred to a real-world bot.

🗣️ As Spencer Huang (NVIDIA Robotics) told me at GTC:“You want to go sim first... We simulate hundreds—sometimes thousands—of robots at once. It’s trial and error at scale.”

Check out my interview with Spencer Huang!

If you care about robots, autonomous vehicles, assistive devices, or even just your future robot vacuum—understanding Physical AI is key.

This is the next frontier in AI. It’s generative (because behaviors are learned, not hand-coded), adaptive, and critical to making robots useful, relatable, and safe in the world we actually live in.

And unlike Olivia Newton-John’s headband, this trend is here to stay.

Check out this resource page from NVIDIA: “What is Physical AI?”

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#PhysicalAI #NVIDIA #IsaacSim #Robotics #Simulation #SimToReal #AIinMotion #DROIDS #JustHereForTheRobots #thedroidsnewsletter #droidsnewsletter #droids

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At the NVIDIA GTC keynote, I watched the crowd leap to its feet (annoyingly, since I was sitting behind them) the moment one of these bots appeared. They’re absolute magic. And people know it—when I posted a short clip of the droids to Imgur, it jumped to 30K views in minutes.

Next year, these same BDX bots will star in The Mandalorian and Grogu. Disney knows what it’s doing. But it’s not just Disney Imagineering at work here. Behind the curtain—powering the personality, balance, and charm—is NVIDIA’s Physical AI.

A Talk with NVIDIA Robotics - “Sim First.”

At GTC, I spoke with Spencer Huang, Product Line Manager for Robotics at NVIDIA. In our interview, he explained how the BDX droids’ fluid movement and emotional presence wouldn't be possible without NVIDIA’s Isaac Sim platform and high-performance GPU-accelerated physics:

“You want to go sim first before going onto the real robot,” Huang said.“We simulate hundreds—sometimes thousands—of robots at once, all learning at the same time through reinforcement learning. It’s trial and error at scale.”

This method allows Disney to train bots to operate on unpredictable surfaces—like gravel or moss—without ever risking hardware damage during training.

“We need a physics-enabled sim that is as true to the real world as possible,” Huang added. “That way, these behaviors have the best chance of deploying successfully to real-world robots.”

BDX Droids: From Animatronics to Autonomy

Disney’s legacy in animatronics is rich. But the BDX droids represent a whole new era. They’re not tethered to tracks or locked into fixed scripts. These bots roam freely and interact with guests dynamically, thanks to behavioral training powered by AI.

In the past, Imagineering would’ve used pre-programmed sequences to simulate lifelike motion. But now, by combining NVIDIA’s reinforcement learning simulations with Disney’s character-building genius, the BDX droids move, emote, and react in ways that make them feel like real personalities.

As Huang put it:

“It’s amazing to see that level of technology come together with a team like Disney, who really knows how to create the personas and these profiles of characters. You really believe you're looking at a Star Wars robot.”

Physical AI and the Future of Empathetic Machines

What’s striking is that these bots aren’t just functional—they’re emotionally expressive. Physical AI allows Disney to dial in behaviors like subtle head tilts, responsive glances, and animated gestures that feel natural and emotionally resonant. It's a blend of motion design and machine learning—robotics with heart.

NVIDIA’s Physical AI doesn't just teach robots how to move. It helps them understand the environments they’re in—and in some cases, the people they’re interacting with. That’s the next frontier: machines that feel more like companions than tools.

From Theme Parks to the Real World

While the BDX droids are showstoppers at Disney, their design philosophy has implications far beyond entertainment. Physical AI is already making its way into logistics, retail, cleanroom automation, and caregiving. As robots become more common in everyday life, emotional responsiveness might become just as important as task completion.

If robots are going to share our sidewalks, homes, and hospitals—they’d better learn how to play nice. And express a little personality.

Final Thoughts

The Disney-NVIDIA partnership on the BDX droids is more than a behind-the-scenes tech story. It’s a signal flare from the future—a glimpse of what happens when creative storytelling and powerful simulation tools collide.

The droids are coming. And thanks to NVIDIA, they’re learning fast.

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Vocabulary Key

* Physical AI: Artificial intelligence that enables machines to perceive, understand, and perform complex actions in the physical world.​

* Reinforcement Learning: A type of machine learning where agents learn to make decisions by receiving rewards or penalties for their actions.​

* Isaac Sim: NVIDIA's simulation platform for developing, testing, and training robots in virtual environments.​

FAQs

What is Physical AI? Physical AI refers to artificial intelligence technologies that allow machines to interact intelligently and autonomously within the physical world.

How did NVIDIA contribute to the development of Disney's BDX droids? NVIDIA provided simulation tools and AI platforms that enabled Disney to train and test the droids in virtual environments before real-world deployment.

Why is simulation important in robotics development? Simulation allows developers to test and refine robot behaviors safely and efficiently, reducing risks and accelerating the development process.

What makes the BDX droids different from previous animatronics? Unlike traditional animatronics, BDX droids are free-roaming and capable of dynamic, responsive interactions, making them more lifelike and engaging.

What are the future implications of Physical AI in robotics? Physical AI has the potential to create robots that are not only functional but also capable of emotional intelligence, enhancing human-robot interactions across various industries.

#Droids #PhysicalAI #NVIDIAIsaac #BDXdroids #StarWarsRobotics #DisneyImagineering #ReinforcementLearning #RobotStorytelling #AIethics #FutureOfRobotics #thedroidsnewsletter #droidsnewsletter #droids

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Aloha, isolatable robot

My 25th‑anniversary surf‑and‑sunscreen getaway took an unexpected detour when I spotted a five‑foot‑eight humanoid wearing nothing but brushed‑aluminum chic. The Tesla showroom anchors Waikiki’s freshly gentrified International Market Place—$1,000 luggage on the left, artisan chocolate on the right, Optimus dead‑center gathering dust.

Fun fact: I was the entire foot‑traffic count for that hour. Great for selfies, bad for morale.

Second fun fact: The original owner of the International Marketplace used to live in the tree house nestled atop the famous Banyan tree. Awesome digs.

Flashback break: Banyan trees & byte counts

I first hit this plaza in ’95 while running PR for Vint Cerf at INET ’95. Back then it was all flea‑market stalls and tiki kitsch. Today? Polished marble—and a silent robot no one greets with aloha.

Meet Optimus (spec sheet, 2025‑edition)

The We Robot Déjà Vu

Last October, Tesla rolled out pole‑mounted, tele‑operated “Decepticons.” Think BB‑8 with a stagehand. The online crowd spotted the puppeteers, and the cringe level spiked. Lesson: we’ll forgive a work‑in‑progress—but not a magic trick that leaks its wires.

Why this showroom matters

* Reality filter: Marketing reels are choreographed; a showroom bot is raw footage.

* Contrast therapy: Figure 01 and Unitree H1 now jog, hop, and self‑balance. Optimus is still bolted to the floor.

* Stakeholder signal: Tesla picked one of the busiest tourist corridors on Earth—yet no staff pitch, no demo loop. Either the bot’s not ready or the marketing budget isn’t. The bot is standing there like a stiff Ken doll gone wrong.

Can I still root for it?

Absolutely. I own two Teslas; the cars are superb even if the CEO’s Twitter feed is a roller‑coaster through a Martian canyon. If Fremont can transfer that “eventual excellence” to bipedal form, Optimus could graduate from showroom prop to factory hand—and maybe caregiver, clean‑room tech, or retail stocker.

Parting shot

After ten quiet minutes, I filmed my goodbye, waved, and left Waikiki’s loneliest bot to its own existential beach vacation. Somewhere inside that aluminum shell might lurk the code to change manufacturing forever. For now, it’s just perfecting the art of standing still. (Hey, it might have a future in Times Square.)

See the actual Tesla bot in this short video clip. Video shot on April 13, 2025 at the Waikiki International Marketplace Tesla showroom.

Bonus: OpenAI’s new o3 model created this comic strip. I think it nailed it.

Additional Resources for Inquisitive Minds:Optimus or Decepticon? Tesla robots remotely operated by humans at ‘We, Robot’. (This revelation isn’t the first time Tesla’s ambitious AI claims have come under scrutiny.) Interesting Engineering. Srishti Gupta.

And, for those who can’t get enough of robots, check out…Deep Learning with the Wolf: Uncanny Valley: Talking to Ameca at the Computer History Museum.

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