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Brain-computer interfaces (BCI) & Robotics lack large-scale datasets. Fourier Robots is developing BCI-driven robotic rehabilitation training. Imagine a stroke patient thinks of moving a hand, and the robot detects the intent to guide the motion.

Rohan Paul

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91,159 görüntüleme • 4 ay önce •via X (Twitter)

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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.

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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Alex Conley, who has a spinal cord injury, was the 2nd patient to undergo neurosurgery at Barrow to receive @Neuralink's N1 Implant as part of the PRIME Study. Now, also enrolled in CONVOY, Alex is the 1st to use the brain-computer interface (BCI) device to control a robotic arm.

Alex Conley, who has a spinal cord injury, was the 2nd patient to undergo neurosurgery at Barrow to receive @Neuralink's N1 Implant as part of the PRIME Study. Now, also enrolled in CONVOY, Alex is the 1st to use the brain-computer interface (BCI) device to control a robotic arm.

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1960s — first brain-computer interface experiments 1998 — first human BCI implant 2005 — paralyzed patient regains movement via BCI 2026 — furry tail, hands-free

1960s — first brain-computer interface experiments 1998 — first human BCI implant 2005 — paralyzed patient regains movement via BCI 2026 — furry tail, hands-free

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German robotics company NEURA Robotics has created NEURA Gym: a large-scale, physical AI gym and training ground where hundreds of robots, including the humanoid 4NE-1, learn through real-world interactions. The company is collaborating with the Neuraverse ecosystem to create the world's largest repository of physical training data, promoting the application of humanoid robots.

German robotics company NEURA Robotics has created NEURA Gym: a large-scale, physical AI gym and training ground where hundreds of robots, including the humanoid 4NE-1, learn through real-world interactions. The company is collaborating with the Neuraverse ecosystem to create the world's largest repository of physical training data, promoting the application of humanoid robots.

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Straight out of a sci-fi movie! Shanghai robotics company Fourier (傅利叶) released a video showing how brain–computer interfaces and exoskeletons are used to sync data and train humanoid robots. Watching humans teach machines—honestly, it gives chills. Video from 顾捷傅利叶CEO Fourier Tuo Liu tphuang Zhang Heqing

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The New England Journal of Medicine (NEJM) today published a peer-reviewed original paper with the results of a landmark clinical trial of our PRIMA brain-computer interface (BCI) retinal implant.

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Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾

Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾

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China’s first humanoid training base, established by the National Humanoid Robot Innovation Center, began operations yesterday. “The training base is essentially a factory that produces data,” said Jiang Lei, chief scientist of the innovation center. The 50k sq. ft. training hub will launch with over a dozen tasks involving 100 robots from more than 10 companies and institutions, including AGIBOT and Fourier. The data gathered here will eventually be available to companies developing embodied AI models. By 2027, the Shanghai facility aims to scale to support 1,000 robots under training.

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Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

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We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

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NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

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This week on the Core Memory pod we sat down with Max Hodak from Science Corp to talk brains, the Merge, the Jennifer Aniston neuron and restoring vision Enjoy! 00:00 – The Matrix inspiration 00:08 – Limitations: physical world vs. simulation 00:32 – The Jennifer Aniston Neuron 02:37 – Who is Max Hodak? Neuralink & Science Corp 03:44 – The vision for Science Corp 05:03 – Restoring vision to the blind 07:26 – Brain-computer interfaces 10:48 – From Duke University to Silicon Valley 14:20 – BCI industry is still in its infancy 19:28 – The takeoff era of human evolution 21:26 – Industrial Revolution to AGI 24:53 – Challenge of making BCI technology profitable 28:11 – AI, mind uploads, and digital immortality 36:19 – Bio-hybrid technology connecting humans to machines 43:00 – The neuroscience of brain-to-brain communication 51:45 – Retinal implants are restoring sight 58:02 – The next frontiers of BCI and AI integration

This week on the Core Memory pod we sat down with Max Hodak from Science Corp to talk brains, the Merge, the Jennifer Aniston neuron and restoring vision Enjoy! 00:00 – The Matrix inspiration 00:08 – Limitations: physical world vs. simulation 00:32 – The Jennifer Aniston Neuron 02:37 – Who is Max Hodak? Neuralink & Science Corp 03:44 – The vision for Science Corp 05:03 – Restoring vision to the blind 07:26 – Brain-computer interfaces 10:48 – From Duke University to Silicon Valley 14:20 – BCI industry is still in its infancy 19:28 – The takeoff era of human evolution 21:26 – Industrial Revolution to AGI 24:53 – Challenge of making BCI technology profitable 28:11 – AI, mind uploads, and digital immortality 36:19 – Bio-hybrid technology connecting humans to machines 43:00 – The neuroscience of brain-to-brain communication 51:45 – Retinal implants are restoring sight 58:02 – The next frontiers of BCI and AI integration

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The Clone hand is a compact, biomimetic robotic hand designed to mimic human anatomy and perform a diverse array of manipulation tasks. Credit: Clone Robotics

The Clone hand is a compact, biomimetic robotic hand designed to mimic human anatomy and perform a diverse array of manipulation tasks. Credit: Clone Robotics

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Fourier has completed their first decade, delivering more than 10,000 robots worldwide. Now, entering the next decade, Fourier wants to use humanoid robots to power rehabilitation, medical treatment, and daily life.

Fourier has completed their first decade, delivering more than 10,000 robots worldwide. Now, entering the next decade, Fourier wants to use humanoid robots to power rehabilitation, medical treatment, and daily life.

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Paralyzed man moves robotic arm with his thoughts | University of California San Francisco Researchers at UC San Francisco have enabled a man who is paralyzed to control a robotic arm through a device that relays signals from his brain to a computer. He was able to grasp, move and drop objects just by imagining himself performing the actions. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted. Until now, such devices have only worked for a day or two. The BCI relies on an AI model that can adjust to the small changes that take place in the brain as a person repeats a movement -- or in this case, an imagined movement -- and learns to do it in a more refined way. "This blending of learning between humans and AI is the next phase for these brain-computer interfaces," said neurologist, Karunesh Ganguly, MD, PhD, a professor of neurology and a member of the UCSF Weill Institute for Neurosciences. "It's what we need to achieve sophisticated, lifelike function." The study, which was funded by the National Institutes of Health, appears March 6 in Cell. The key was the discovery of how activity shifts in the brain day to day as a study participant repeatedly imagined making specific movements. Once the AI was programmed to account for those shifts, it worked for months at a time. Location, location, location Ganguly studied how patterns of brain activity in animals represent specific movements and saw that these representations changed day-to-day as the animal learned. He suspected the same thing was happening in humans, and that was why their BCIs so quickly lost the ability to recognize these patterns. Ganguly and neurology researcher Nikhilesh Natraj, PhD, worked with a study participant who had been paralyzed by a stroke years earlier. He could not speak or move. He had tiny sensors implanted on the surface of his brain that could pick up brain activity when he imagined moving. To see whether his brain patterns changed over time, Ganguly asked the participant to imagine moving different parts of his body, like his hands, feet or head. Although he couldn't actually move, the participant's brain could still produce the signals for a movement when he imagined himself doing it. The BCI recorded the brain's representations of these movements through the sensors on his brain. Ganguly's team found that the shape of representations in the brain stayed the same, but their locations shifted slightly from day to day. From virtual to reality Ganguly then asked the participant to imagine himself making simple movements with his fingers, hands or thumbs over the course of two weeks, while the sensors recorded his brain activity to train the AI. Then, the participant tried to control a robotic arm and hand. But the movements still weren't very precise. So, Ganguly had the participant practice on a virtual robot arm that gave him feedback on the accuracy of his visualizations. Eventually, he got the virtual arm to do what he wanted it to do. Once the participant began practicing with the real robot arm, it only took a few practice sessions for him to transfer his skills to the real world. He could make the robotic arm pick up blocks, turn them and move them to new locations. He was even able to open a cabinet, take out a cup and hold it up to a water dispenser. Months later, the participant was still able to control the robotic arm after a 15-minute "tune-up" to adjust for how his movement representations had drifted since he had begun using the device. Ganguly is now refining the AI models to make the robotic arm move faster and more smoothly, and planning to test the BCI in a home environment. For people with paralysis, the ability to feed themselves or get a drink of water would be life changing. Ganguly thinks this is within reach. "I'm very confident that we've learned how to build the system now, and that we can make this work," he said. Read more:

Paralyzed man moves robotic arm with his thoughts | University of California San Francisco Researchers at UC San Francisco have enabled a man who is paralyzed to control a robotic arm through a device that relays signals from his brain to a computer. He was able to grasp, move and drop objects just by imagining himself performing the actions. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted. Until now, such devices have only worked for a day or two. The BCI relies on an AI model that can adjust to the small changes that take place in the brain as a person repeats a movement -- or in this case, an imagined movement -- and learns to do it in a more refined way. "This blending of learning between humans and AI is the next phase for these brain-computer interfaces," said neurologist, Karunesh Ganguly, MD, PhD, a professor of neurology and a member of the UCSF Weill Institute for Neurosciences. "It's what we need to achieve sophisticated, lifelike function." The study, which was funded by the National Institutes of Health, appears March 6 in Cell. The key was the discovery of how activity shifts in the brain day to day as a study participant repeatedly imagined making specific movements. Once the AI was programmed to account for those shifts, it worked for months at a time. Location, location, location Ganguly studied how patterns of brain activity in animals represent specific movements and saw that these representations changed day-to-day as the animal learned. He suspected the same thing was happening in humans, and that was why their BCIs so quickly lost the ability to recognize these patterns. Ganguly and neurology researcher Nikhilesh Natraj, PhD, worked with a study participant who had been paralyzed by a stroke years earlier. He could not speak or move. He had tiny sensors implanted on the surface of his brain that could pick up brain activity when he imagined moving. To see whether his brain patterns changed over time, Ganguly asked the participant to imagine moving different parts of his body, like his hands, feet or head. Although he couldn't actually move, the participant's brain could still produce the signals for a movement when he imagined himself doing it. The BCI recorded the brain's representations of these movements through the sensors on his brain. Ganguly's team found that the shape of representations in the brain stayed the same, but their locations shifted slightly from day to day. From virtual to reality Ganguly then asked the participant to imagine himself making simple movements with his fingers, hands or thumbs over the course of two weeks, while the sensors recorded his brain activity to train the AI. Then, the participant tried to control a robotic arm and hand. But the movements still weren't very precise. So, Ganguly had the participant practice on a virtual robot arm that gave him feedback on the accuracy of his visualizations. Eventually, he got the virtual arm to do what he wanted it to do. Once the participant began practicing with the real robot arm, it only took a few practice sessions for him to transfer his skills to the real world. He could make the robotic arm pick up blocks, turn them and move them to new locations. He was even able to open a cabinet, take out a cup and hold it up to a water dispenser. Months later, the participant was still able to control the robotic arm after a 15-minute "tune-up" to adjust for how his movement representations had drifted since he had begun using the device. Ganguly is now refining the AI models to make the robotic arm move faster and more smoothly, and planning to test the BCI in a home environment. For people with paralysis, the ability to feed themselves or get a drink of water would be life changing. Ganguly thinks this is within reach. "I'm very confident that we've learned how to build the system now, and that we can make this work," he said. Read more:

Owen Gregorian

38,092 görüntüleme • 1 yıl önce