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Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are...

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Lukas Ziegler

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20,898 просмотров • 6 месяцев назад •via X (Twitter)

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Collaborating like an ant colony Blue Worm Robotics from Xi'an has released a wheeled humanoid robot solution for warehouse automation. The wheeled humanoid robots move freely and quickly among warehouse shelves and workstations, using their lifting bodies to move, transfer, and sort goods. The robots collaborate with each other, and multiple robots can even work together to assemble another robot. As the collaborative tasks become more complex, swarm intelligence will emerge.

CyberRobo

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I'll tell it like it is: they're lying. Robots are getting better and better, and the goal now is not to support human labor, as suggested in the video, but in the long term they will replace human labor. In a market economy, profit is the only thing that counts, whether you like to criticize that or not. The fact is, however, that robots will be cheaper than human labor and will therefore be preferred. Moreover, robots are getting better and better, they will work longer hours than humans, are less prone to errors, do not fall ill and do not need vacations. They are superior to human labor.
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I'll tell it like it is: they're lying. Robots are getting better and better, and the goal now is not to support human labor, as suggested in the video, but in the long term they will replace human labor. In a market economy, profit is the only thing that counts, whether you like to criticize that or not. The fact is, however, that robots will be cheaper than human labor and will therefore be preferred. Moreover, robots are getting better and better, they will work longer hours than humans, are less prone to errors, do not fall ill and do not need vacations. They are superior to human labor.

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svt is telling us that they’re proud of their music combinations (classic and tech) but, they emphasize that the real maestro, who gives “command” in music is HUMAN! [EXPLANATION — 1] watch the clip where after wonjun fighting the human robots, maestro hoshi got captured jeonghan then made those human robots flew away and svt taking over the stage (previously occupied by robots) • the human robot soldiers get a notice “moving too fast”. this shows that human, are more capable than tech/robots (in this case, artistry) • notice that the dancers using robotic hands, shows the tech is a “tool” to help human, but the control is still in human itself
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svt is telling us that they’re proud of their music combinations (classic and tech) but, they emphasize that the real maestro, who gives “command” in music is HUMAN! [EXPLANATION — 1] watch the clip where after wonjun fighting the human robots, maestro hoshi got captured jeonghan then made those human robots flew away and svt taking over the stage (previously occupied by robots) • the human robot soldiers get a notice “moving too fast”. this shows that human, are more capable than tech/robots (in this case, artistry) • notice that the dancers using robotic hands, shows the tech is a “tool” to help human, but the control is still in human itself

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A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

42,565 просмотров • 4 месяцев назад

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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This perspective will be more intuitive. Fourier is building a data acquisition and training platform that integrates non-invasive brain-computer interfaces and humanoid robots. This wealth of data will enhance the intelligence level of GR-3. The goal is for GR-3 to accurately identify the brain intention signals of stroke patients during future rehabilitation training and respond in real time to guide patients in performing rehabilitation movements. For example, lifting the left hand, picking up a block from the table, etc. Of course, GR-3 can also become a life assistant controlled by a human BCI. Imagine that people with mobility impairments could also issue commands to GR-3 via BCI to open the refrigerator and get a drink.
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This perspective will be more intuitive. Fourier is building a data acquisition and training platform that integrates non-invasive brain-computer interfaces and humanoid robots. This wealth of data will enhance the intelligence level of GR-3. The goal is for GR-3 to accurately identify the brain intention signals of stroke patients during future rehabilitation training and respond in real time to guide patients in performing rehabilitation movements. For example, lifting the left hand, picking up a block from the table, etc. Of course, GR-3 can also become a life assistant controlled by a human BCI. Imagine that people with mobility impairments could also issue commands to GR-3 via BCI to open the refrigerator and get a drink.

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Schaeffler is bringing UK's humanoid robots from concept to factory reality. Within the next 5 years, hundreds of humanoid robots will be deployed in Schaeffler's global factories, with beta versions first implemented in 2026–2027. Schaeffler is also becoming the preferred supplier of joint actuators for the humanoid wheeled platform and is jointly exploring next-generation actuator architectures with its partner. More importantly, the two companies will collaborate deeply on data and skills: continuously accumulating real-world manufacturing data through teleoperation, synthetic data, and peripheral data acquisition robots to train AI models, enabling humanoid robots to learn faster and perform more reliably in the factory.

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Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

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44,368 просмотров • 29 дней назад

Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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🇨🇦 ROBOT HANDS JUST LEVELED UP IN CANADA Sanctuary AI showed off a robotic hand that uses hydraulic power to grip, twist, and move like a real one. The hand can handle delicate objects, like dice, without crushing them, proving robots are getting scary precise. Hydraulic actuation gives the robot smoother, stronger control, making it better for heavy industrial work and tricky small tasks. This could change how robots work in factories, warehouses, or even in jobs too dangerous for humans. The future of robot hands? Less clunky claws, more human-like grip power with machine muscle. Source: Wevolver

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54:58

The full LadderMan podcast episode is finally live! 🪜 LadderMan is one of the most interesting papers recently: instead of just walking on flat ground or doing flashy demos, it tackles a very real-world capability — letting humanoid robots climb ladders with perception, planning, and whole-body control. To me, this is exactly the kind of work that pushes robots closer to actually operating in human environments. Huge thanks again to Siheng Zhao , Chris Paxton, The Humanoid Hub, and Zifan Xu for joining the discussion. The full episode is a bit long, so I’ll be cutting some of the best moments into shorter clips and sharing them soon.

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Don't sleep on humanoid robots. Humanoid robots are going to play a profound role in our lives, and it will likely be sooner than most expect. I discuss some pivotal questions with jimmah in my effort to gain insight and clarity: - How do you evaluate Tesla's (and other companies') progress with humanoid robots? - Who will be the winner? Or will there be multiple big winners? - How long will it take until we see millions of humanoid robots produced annually? - What's harder for humanoid robots - the hardware of software? - How can we track progress across humanoid robot companies? - What's the next big milestones or demonstrations Tesla can show? If you're going to spend an hour listening/watching anything online this week, I highly recommend this!

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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Elon Musk: Optimus is awesome! “Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics, like the Optimus humanoid robot. Optimus is awesome! There's going to be so many humanoid robots and robots of all sizes and shapes. But my prediction is that there will be more humanoid robots by far than all other robots combined by maybe an order of magnitude, like a big difference.” Interview at Y Combinator, June 16, 2025
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Elon Musk: Optimus is awesome! “Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics, like the Optimus humanoid robot. Optimus is awesome! There's going to be so many humanoid robots and robots of all sizes and shapes. But my prediction is that there will be more humanoid robots by far than all other robots combined by maybe an order of magnitude, like a big difference.” Interview at Y Combinator, June 16, 2025

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