Tag: embodied AI

Prince Mario-Max Schaumburg-Lippe: Meet RP1, the Open-Source Humanoid Robot Anyone Can Study

At this year’s IROS robotics conference, one booth had a sign that said “Kick Me.” Visitors did. They pushed the robot, shoved it, kicked its legs — and the machine adjusted its posture, caught its balance, and kept standing. The robot was the RP1, and its maker, RoboParty, just unveiled it as what it calls the world’s first high-performance, full-stack open-source bipedal humanoid.

The “open-source” part is the story. Most humanoid robots are black boxes: proprietary hardware, secret software, research papers that tell you what the robot did but never how. The RP1 goes the other direction. RoboParty plans to progressively release the mechanical designs, motion-control systems, simulation environments, SDKs, training tools and its PartyOS development foundation. A lab that wants to study humanoid locomotion won’t need a nine-figure budget. It will need a download.

What the machine can do

The specs read like a serious research platform, not a toy. The RP1 delivers peak joint torque up to 160 N·m through in-house-developed Romomo actuator modules — enough power for dynamic movement, not just careful laboratory steps. A real-time motion-control system keeps it responsive, and the “Kick Me” demonstration at IROS put that responsiveness on public display. Disturbance recovery — staying upright when the world pushes back — is one of the hardest problems in humanoid robotics, and RoboParty chose to make it the demo.

Under the hood sits PartyOS, the company’s open R&D foundation for humanoid robotics. It integrates UFO, a training framework that discovers motor skills through unsupervised reinforcement learning — skill transitions, disturbance recovery, fall recovery — without relying on predefined motion trajectories. In plain terms: instead of engineers programming every movement by hand, the robot learns to move by trying, failing and improving in simulation, then transfers those skills to its real body.

The unveiling builds on RPO, or ROBOTO ORIGIN, RoboParty’s fully open-source humanoid project launched in January 2026. That project has already collected more than 2,500 GitHub stars — a respectable following for hardware, where open-source communities are far rarer than in software. The RP1 is the commercial-grade next step: the same open philosophy, with performance aimed at serious embodied-AI research.

Why open source matters for robots

Consider what open source did for software. Linux, TensorFlow, PyTorch — the shared foundations let thousands of teams build on each other’s work instead of reinventing it. Robotics never got that treatment, because robots are physical: expensive to build, hard to ship, and every lab’s hardware is slightly different. Research has been fragmented by necessity.

A capable open-source humanoid changes the equation. If hundreds of labs run the same platform, results become comparable. A locomotion paper from Tokyo can be reproduced in Berlin. Improvements to balance control, funded by one university, benefit every team on the platform. RoboParty is betting that this network effect — the same one that made open-source software unstoppable — will work for humanoid bodies too.

The timing is right. IDC estimates nearly 25,000 humanoid robots shipped globally in the first half of 2026, up 432% year over year, with Chinese vendors like Agibot and Unitree leading on volume. The manufacturing ramp is real. But most of those robots went to research labs, education, displays and data centers — not factory floors. The industry’s open question isn’t who can build the most robots. It’s who can make robots genuinely useful. Open platforms accelerate exactly that search, by putting capable hardware in the hands of the people most likely to find the answer.

The competition isn’t sitting still

The RP1 enters a crowded field. Boston Dynamics just gave its Atlas a dexterous new hand with 13 degrees of freedom, aimed at real factory work at Hyundai’s Georgia plant. Dyna Robotics’ Taku is already doing unsupervised laundry and kitchen workflows in hotels and restaurants. Agility Robotics is partnering on safety infrastructure to scale its Digit platform into warehouses.

RoboParty isn’t trying to out-manufacture those companies. It’s playing a different game: become the platform the researchers use, the way a generation of roboticists grew up on the same open-source software stack. If the RP1 becomes the default humanoid in university labs, RoboParty wins even if it never sells a robot to a factory.

What it means for researchers, industry and everyone else

For researchers and educators, the RP1 lowers the barrier to humanoid work dramatically. A graduate student with a good idea about balance control no longer needs her university to buy a million-dollar robot or build one from scratch. That democratization is how fields accelerate — the best ideas often come from the labs with the least money.

For industry, open-source humanoids are a talent pipeline. Every student who learns robotics on an RP1 is an engineer who can be hired to work on commercial platforms. Companies that once guarded their hardware are discovering what software firms learned decades ago: open foundations grow the ecosystem that feeds you.

For everyone else, the “Kick Me” demo is the detail to remember. A robot that can be shoved and stay standing is a robot that’s getting close to surviving the real world — cluttered, unpredictable, full of things that push back. The RP1 won’t be folding your laundry tomorrow. But somewhere in a university lab, a researcher is downloading its designs tonight. And that’s how the future usually starts: not with a product launch, but with a download.

Prince Mario-Max Schaumburg-Lippe: A $18,000 Humanoid Robot Just Debuted in the U.S.

Another humanoid robot maker just planted a flag in America, and this one brought a price tag meant to get attention.

Shenzhen-based Astribot is making its North American debut this week at IROS 2026, the International Conference on Intelligent Robots and Systems running September 27 through October 1 at Pittsburgh’s David L. Lawrence Convention Center. The company is showing off its T1 humanoid robot alongside its full Physical AI stack, the first time it has brought the integrated platform to a North American audience.

The headline number: U.S. pricing starts at $18,000, with orders open now and immediate delivery available. In a market where humanoid robots often cost as much as a car or remain perpetually “coming soon,” a buy-it-today price under twenty grand is a statement.

## One system, not three

Astribot’s pitch is architectural. The company calls it “Design for AI”: the AI models, the embodied operating system, and the cable-driven robotic body are co-designed as a single system rather than bolted together afterward. It sounds like marketing until you watch what the robot does.

Running on the company’s Lumo-2 model, the T1 has demonstrated autonomous tidying, including sorting miscellaneous items into a backpack. That task sounds trivial until you think about what’s involved: deciding what goes where, then handling deformable objects like fabric with enough dexterity not to mangle them. Lumo-1 introduced the company’s Reasoning-Action Foundation Model framework; Lumo-2 pushes into latent world-action modeling for more complex physical tasks.

The hardware backs it up. The T1 stands about 1.55 meters tall, weighs around 66 kilograms, and offers 23 degrees of freedom excluding the end effectors, with a payload of up to 5 kilograms per arm. The cable-driven architecture gives it compliant, dexterous manipulation and fast movement, while feeding richer physical interaction data back into the AI stack. Practical boxes are checked too: automatic charging, quick battery swaps, and modular end effectors, computing modules, and sensors that can be exchanged for different jobs.

## Built for builders

Astribot is clearly aiming at developers first. The T1 ships with SDK and API access covering joint control, Cartesian motion, whole-body coordination, and sensor data. The embodied operating system includes meta-packages and skill libraries for orchestrating agentic behaviors, plus a natural-language interface that can generate a deployable robot application from a single-line requirement.

The developer bet already has evidence behind it. At the second Astribot OS Hackathon in Beijing, which concluded September 21, fifteen teams built and demonstrated more than ten functional T1 applications in just 36 hours. That’s the kind of velocity that turns a robot from a product into a platform.

The learning loop is deliberate rather than magical. The T1 doesn’t retrain itself live during deployment. Instead, Astribot collects multimodal data from robot operation and teleoperation, curates it, retrains models like Lumo-2, and pushes updated skills over the air or on-site. Customers can opt to contribute their own operational data back into the cycle. It’s a flywheel, and every deployed robot makes the next one smarter.

## Why Pittsburgh, why now

The location of the debut is part of the message. Pittsburgh’s robotics corridor, anchored by Carnegie Mellon, has become one of the densest concentrations of robotics talent in the world, and IROS is the field’s flagship conference. Unveiling the T1’s North American debut here puts Astribot directly in front of the researchers, developers, and investors who decide which platforms get built on.

The timing helps too. Global humanoid shipments are surging, with IDC tracking a 432 percent year-on-year jump in the first half of 2026, and the application mix is diversifying beyond research into industrial and commercial use. Astribot is arriving just as the market shifts from curiosity to procurement. An $18,000 developer-ready humanoid landing in that moment isn’t only a product launch. It’s a bid for the platform position.

## What it means

For travelers and consumers, the $18,000 price point is the story. Humanoid robots have lived in two worlds: six-figure industrial machines and research projects. A capable, developer-friendly humanoid at the price of a used car starts to look like something a small business, a lab, or eventually a household could actually buy. The home applications are still in training, but the direction is unmistakable.

For cities like Pittsburgh, hosting IROS matters. The robotics corridor from Carnegie Mellon outward keeps attracting global players who want to be near the talent. Astribot choosing IROS for its North American debut is a vote of confidence in that ecosystem.

For investors, watch the platform play. Hardware margins on an $18,000 robot are fine, but the real prize is the developer ecosystem: the skills library, the data flywheel, the app store dynamics. The company that owns the platform developers build on tends to win the category. Astribot just opened its doors to American builders. The 36-hour hackathon suggests they won’t wait long to walk through.

For more on the humanoid race, see our [Breaking News coverage](https://newstodayworld.org/category/breaking-news/), including [global humanoid robot shipments surging 432% in six months](https://newstodayworld.org/breaking-news/2026/09/30/humanoid-robot-shipments-surge-432-in-six-months/) and [Momenta’s plans for thousands of robotaxis in Dubai and Europe](https://newstodayworld.org/breaking-news/2026/09/30/momenta-plans-thousands-of-robotaxis-for-dubai-europe/).

Prince Mario-Max Schaumburg-Lippe: New Motion-Capture Lab Trains Humanoids to Move Naturally

Everyone obsesses over robot brains. The bodies are the hard part. On September 30, Innodata Inc. (Nasdaq: INOD) announced the opening of a new research and development lab in New Jersey dedicated to one of the toughest problems in robotics: teaching humanoids to move like humans.

The lab was built with Vicon, the motion-capture leader, which consulted on the lab’s design. It uses high-precision, low-latency infrared optical tracking cameras that measure movement down to sub-millimeter accuracy, a leap beyond wearable IMUs or single-camera video analysis.

The purpose: generate training data for humanoids, industrial robots, and other “physical AI,” and independently validate robot performance data. Here’s the key distinction. Many data providers infer 3D motion from 2D video: essentially guessing depth. Innodata captures 3D directly from the body. No guessing.

Frank Tanner, the company’s VP of robotics and physical AI, put it bluntly: “There’s just no substitute for direct 3D motion capture… When you’re training a humanoid that weighs almost 200 pounds, your readings can’t be in the ballpark. They need to be precise.”

Why “Close Enough” Doesn’t Cut It

A chatbot that misplaces a comma is a punchline. A 200-pound humanoid that misplaces a footstep is a hazard. That’s why sub-millimeter precision matters.

Think about what walking actually requires. Balance, timing, weight transfer, joint angles: hundreds of tiny coordinated adjustments per second. A human does it without thinking. A robot has to learn every one of them, and “approximately right” compounds into falling over. Or worse.

This is the ground-truth problem of physical AI. Language models trained on the internet, which, as one founder put it this week, is now exhausted as a data source. “The internet is exhausted, the physical world is not.” The next data centers, in a sense, are motion-capture studios.

The lab also serves a second role that’s easy to overlook: independent validation. As humanoid robots like Agility’s Digit get deployed in warehouses and beyond, someone needs to verify that a robot actually performs as claimed. A precision mocap lab is the scale that weighs the claim.

Hollywood Tech, Repurposed

There’s a lovely symmetry here. Motion capture is the technology behind Gollum, Avatar, and a thousand video game characters, actors in dotted suits performing while cameras record every twitch. Now the same rigs are being pointed at the next generation of robots, teaching machines the movement vocabulary that actors spent decades perfecting.

It’s also a New Jersey story, which is worth a smile. The Garden State, not exactly known as a robotics hub, now hosts a facility generating some of the most precise movement data on Earth. Innovation has a way of showing up where you least expect it.

The timing lines up with the broader physical-AI surge. General Intuition’s $220 million raise for its action foundation model landed the same day, pairing the funding wave with the data wave. Money is flowing into physical AI, and labs like Innodata’s are the unglamorous infrastructure that makes the glamorous demos possible.

What This Unlocks

Better movement data means robots that walk more naturally, handle objects more delicately, and operate safely around people. The downstream effects are practical and positive: warehouse robots that don’t damage goods, industrial robots that work alongside humans instead of behind cages, and eventually assistive robots with the dexterity to help in homes and hospitals.

None of that happens without ground truth. A robot can’t learn to move from videos that approximate depth. It needs to know exactly where a knee was, to the fraction of a millimeter, at the exact millisecond it bore weight. That’s what this lab produces: the truth about movement, measured and digitized.

The Bigger Picture

Physical AI’s bottleneck was never the algorithms alone — it was always the data. Language had the internet; movement had nothing comparable. Facilities like Innodata’s New Jersey lab are building that dataset from scratch, one captured step at a time.

And the applications go well beyond humanoids. Industrial robots that assist rather than replace, arms that hand tools to technicians, mobile platforms that restock shelves, all need the same movement vocabulary. Even autonomous freight depends on robotic systems that handle cargo with precision. Every one of these machines gets safer and more capable when its training data is measured rather than estimated.

There’s a validation angle too. As robots move from labs to warehouses, factories, and eventually public spaces, independent measurement becomes the trust layer. A company buying a fleet of humanoids wants proof of performance, not marketing. A lab that can measure a robot’s gait to the sub-millimeter is the auditor the industry didn’t know it needed, and it’s arriving just as the deployments begin.

The robots are coming, and they’re coming with better posture than we’d expect. Sub-millimeter by sub-millimeter, the physical world is becoming training data. Machines are finally learning to move through it like they belong here.

Prince Mario-Max Schaumburg-Lippe: General Intuition Raises $220M to Teach AI the Real World

The next frontier of AI isn’t a smarter chatbot. It’s AI that can see, move, and act: pick up a box, navigate a warehouse, climb a set of stairs. And on September 30, one of the biggest bets on that future got a lot bigger: New York–based General Intuition raised $220 million at a $6.2 billion valuation.

The backers are a who’s who of venture capital: Valor Equity Partners, Atreides Management, 776, Point72 Ventures, Khosla Ventures, and General Catalyst. One of the largest physical-AI raises of the month, and a clear signal that serious money is following the agentic-AI wave into the real world.

The capital is earmarked for GPU cluster acquisitions, accelerated foundation-model training, expanded machine learning and reinforcement learning research teams in New York, and commercial infrastructure spanning both virtual gaming systems and physical robotics platforms.

Why Games Are the Gym for Robots

The company’s core idea is elegant. Games were the original training ground for modern AI: think DeepMind learning Atari, AlphaGo conquering Go. General Intuition is betting that millions of hours of gameplay telemetry is the bridge to robots that function in messy reality.

Here’s the clever bit. Instead of hand-labeling the physical world, an expensive, slow, painstaking process, learn intent from players who already demonstrate it. Every game session is a human showing, moment by moment, what they meant to do: navigate this space, grab that object, avoid that obstacle.

General Intuition’s tech is a multi-modal “action foundation model” trained on massive proprietary datasets of multi-angle gameplay video combined with player input telemetry and real-time execution matrices. The model learns spatial navigation, physics interactions, and operational intent, the same skills a robot needs, and drives both autonomous agents in simulations and humanoid robots in the real world.

In other words: the training data is hiding in play. Humans already generate exquisitely detailed demonstrations of physical intent every time they game. General Intuition is just harvesting it.

The Embodied AI Wave Is Building

This raise doesn’t exist in isolation. Physical AI, the industry term for AI that acts in the physical world, is having its moment. Humanoid robots like Agility’s Digit are getting stronger, safer, and more capable. Driverless trucks are on public roads. Robotaxi fleets are scaling fast.

Each of those machines needs a brain that understands physics, not just language. A chatbot can be wrong and it’s a joke; a 200-pound humanoid can be wrong and it’s a lawsuit. The bar for “good enough” in physical AI is brutally higher than in text, which is why the training approach matters so much.

General Intuition’s angle, learning from demonstrated intent at massive scale, sidesteps the biggest bottleneck in robotics: labeled real-world data is scarce and expensive. Gameplay telemetry is abundant and rich. If the transfer from virtual to physical works, it’s a shortcut around years of slow data collection.

What $6.2 Billion Says About the Moment

Valuations this size say investors believe embodied AI is following the same arc as language AI: a period of expensive foundational work, then a sudden unlock. The GPU clusters, the expanded research teams, the commercial infrastructure across gaming and robotics: this is a company building the full stack, not a demo.

The New York angle is nice too. The company is expanding its ML and reinforcement learning research teams in the city, planting a flag for physical AI on the East Coast in an industry that tends to default to the Bay Area.

Where the Robots Go First

The commercial infrastructure piece of the raise deserves attention. General Intuition isn’t just training models — it’s building the deployment pipeline across virtual gaming systems and physical robotics platforms. The near-term beachhead is likely the warehouse: structured enough to be tractable, labor-hungry enough to pay for automation. Longer term, the same action models that navigate a game level could navigate a disaster site or a factory floor.

That’s the bet the investors are making with $220 million: that “action” becomes a foundation-model category the way language did, and that the company holding the best action model holds a position worth far more than $6.2 billion. It’s early. But every major AI platform started with someone training an expensive model on data nobody else thought to collect.

The Optimist’s View

Picture where this leads. Robots that learn movement the way humans do, by watching and doing at scale, could take on the dull, dirty, and dangerous work that’s hard to staff: warehouse shifts, disaster cleanup, elder care assistance. The path from a game controller to a helpful humanoid is longer than a press release makes it sound, but the direction is right, and $220 million is a serious down payment.

General Intuition’s bet is simple and, in retrospect, may look obvious: the internet taught AI to think; play will teach it to move. The funding announced today suggests a lot of very smart investors agree.

Prince Mario-Max Schaumburg-Lippe: Humanoid Robot Shipments Surge 432% in Six Months

The humanoid robot industry just had its breakout half-year.

New data released this week shows global humanoid robot shipments surging to nearly 25,000 units in the first half of 2026, up 432 percent from the same period last year. The market topped $740 million, up 323 percent. And for the first time, there’s a new company on top of the world.

Agibot has overtaken Unitree Robotics to become the largest humanoid robot maker on the planet by shipments — and by revenue, according to IDC’s tracker. A year ago, Unitree held the crown. The reshuffle happened that fast.

The numbers behind the surge

Two research firms released trackers this week and, while their methodologies differ, both point the same direction. IDC counts roughly 25,000 units shipped globally in H1 2026, up 432.1 percent year over year. Smart Analytics Global (SAG) offers a more conservative tally: about 19,100 units, up 272 percent. Either way, the industry more than tripled in a year.

China is the engine. IDC says the Chinese market alone shipped more than 19,000 units, up 426 percent, accounting for roughly 78 percent of the global total. SAG’s estimate is even more lopsided, crediting Chinese manufacturers with over 97 percent of global volume. The supply chain story explains why: China has the component makers, the AI model companies, and the system integrators all iterating together, which keeps driving costs down and production up.

IDC was impressed enough to raise its long-term forecast, now projecting global humanoid shipments to exceed 750,000 units by 2030, about 50 percent higher than its previous estimate.

How Agibot took the lead

Agibot shipped more than 8,600 units in the first half of the year, capturing 35 percent of the global market and over 45 percent of the Chinese market. That’s tenfold growth. Unitree still grew 170 percent to about 5,900 units and a 31 percent global share, with its G1 model doing strong business in research and education. When your rival grows 170 percent and you still lose the top spot, you know the market is moving fast.

Together, the two Chinese companies now hold more than half the global market. Behind them, a cluster of other Chinese firms — Booster Robotics, UBTECH, Galaxy General, Leju — is filling out the leaderboard.

Agibot says it’s now shifting from pure production volume to deployment. At its 2026 partner conference, the company rolled out what it calls seven deployment-ready productivity solutions: production-line loading and unloading, industrial transport, logistics sorting, guided tours and shopping assistance, service retail stations, security inspection, and commercial and industrial cleaning. Days earlier, AGIBOT delivered its 20,000th robot off the production line to Chimelong Spaceship Park, where more than 300 of its robots are now working across entertainment, education, visitor services, and hotel operations. The factory milestone and the theme-park deployment landed in the same week. That timing was not an accident.

Where the robots are actually going

The most encouraging number in the reports isn’t a shipment total. It’s the application mix.

In the first half of 2026, research and education, performance and display demos, and government data centers together accounted for 69 percent of shipments. That’s still a lot of robots doing research projects and stage shows. But it’s down from 84 percent for full-year 2025. The industry is diversifying out of the lab and into real work.

SAG’s report is blunt about where the real commercialization path runs: manufacturing, logistics, and warehousing. Structured environments, clearly defined tasks, measurable productivity. Automotive plants and electronics factories are the beachheads. In those settings, a humanoid that can load a line, sort a tote, or tend a machine earns its keep in numbers a CFO can check.

And the consumer market is finally appearing on the ledger. Vendors are shipping smaller, cheaper humanoids through e-commerce channels for children’s education and personal companionship. The second half of this year is expected to bring more of them. The robot that folds your laundry is still a dream. The robot that keeps your kid company while teaching math is a product listing.

What it means

For travelers and consumers, the 432 percent number is the sound of a price curve bending. Tenfold growth at Agibot means manufacturing scale, and manufacturing scale means the $3,000-$4,000 humanoid is no longer a fantasy — startups are already advertising preorders in that range. The home robot won’t arrive all at once. It’ll arrive as a tutor, a companion, a very expensive toy, and then one day it just lives in your house.

For cities and industries, the message is that the deployment phase has started. The robots leaving factories now are going to warehouses, production lines, and public venues, not just university labs. Regions that build the service infrastructure — maintenance, integration, training — will capture the economic upside of the next wave.

For investors, IDC’s raised 2030 forecast is the headline: 750,000-plus units a year within four years. The Agibot-Unitree reshuffle is the warning label. In a market growing this fast, today’s leader is one product cycle away from being lapped. Bet on the supply chain and the deployment pipeline, not the logo.

For more on robots and autonomy scaling up, see our Breaking News coverage, including Waymo’s robotaxi fleet surging 49 percent in Texas and Germany’s first cab-less driverless truck on public roads.