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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.

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Rohan Paul

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91,321 görüntüleme • 7 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.
0:48

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.

CyberRobo

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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.
4:15

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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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.
1:39

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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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

Shenzhen Channel

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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! 🦾
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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! 🦾

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21,627 görüntüleme • 1 yıl önce

Our brain-to-voice synthesis brain-computer interface paper was published in nature today! This neuroprosthesis synthesized the voice of a man with ALS instantaneously, enabling him to ‘speak’ flexibly and modulate the prosody of his BCI-voice. 1/7 Paper:
1:04

Our brain-to-voice synthesis brain-computer interface paper was published in nature today! This neuroprosthesis synthesized the voice of a man with ALS instantaneously, enabling him to ‘speak’ flexibly and modulate the prosody of his BCI-voice. 1/7 Paper:

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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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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.”
2:16

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.”

TBPN

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Love this. A boon for people with disabilities: A robotic hand that augments human capability. The creators say EMG control is coming next. --- Integrating BCI and AI models in the future will create a powerful human-machine intelligent agent.
0:37

Love this. A boon for people with disabilities: A robotic hand that augments human capability. The creators say EMG control is coming next. --- Integrating BCI and AI models in the future will create a powerful human-machine intelligent agent.

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If the end goal of robot hands is to perform human motion, then we should optimize the hardware design with human motion - and on a large scale! We can generate both a high-dof generalist hand, and also low-dof specialized hands from human demonstration.
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If the end goal of robot hands is to perform human motion, then we should optimize the hardware design with human motion - and on a large scale! We can generate both a high-dof generalist hand, and also low-dof specialized hands from human demonstration.

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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

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There’s a 400k welder labor shortage in the US alone and the average welder is 55 years old Path Robotics is training robot dogs and other general purpose robots to weld

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82,042 görüntüleme • 1 ay önce

Awesome! This is literally a superhuman hand. 🤖✋ ChangingTek Robotics just dropped a dexterous hand that actually morphs between left and right hand configurations on the fly. It uses cable-driven tech to pull off shapes and reverse bends that human hands can't even dream of doing. The power is real too, with a 50N grip force and 10N at the fingertips while staying precise within 0.1N. It is kind of like a rigid octopus, meaning it is tough enough to crank a wrench or screwdriver but smooth enough to grab a drink. Having this much range of motion in a single hand basically doubles the efficiency of any robotic arm in tight spots. Source: ChangingTek Robotics #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #ChangingTek #DexterousHand #Bionics
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Awesome! This is literally a superhuman hand. 🤖✋ ChangingTek Robotics just dropped a dexterous hand that actually morphs between left and right hand configurations on the fly. It uses cable-driven tech to pull off shapes and reverse bends that human hands can't even dream of doing. The power is real too, with a 50N grip force and 10N at the fingertips while staying precise within 0.1N. It is kind of like a rigid octopus, meaning it is tough enough to crank a wrench or screwdriver but smooth enough to grab a drink. Having this much range of motion in a single hand basically doubles the efficiency of any robotic arm in tight spots. Source: ChangingTek Robotics #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #ChangingTek #DexterousHand #Bionics

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13,855 görüntüleme • 6 ay önce

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉
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Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

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"When you go to spaces like VR or BCI, everybody knows each other, and that means there's a high density high quality founders, and whoever makes it work in the bear market -- they are going to crush it when the market turns better." Felix Hartmann explains why some of the most compelling asymmetric opportunities lie in ecosystems like VR and brain-computer interfaces. Watch the full episode of Angels to Allocators:
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"When you go to spaces like VR or BCI, everybody knows each other, and that means there's a high density high quality founders, and whoever makes it work in the bear market -- they are going to crush it when the market turns better." Felix Hartmann explains why some of the most compelling asymmetric opportunities lie in ecosystems like VR and brain-computer interfaces. Watch the full episode of Angels to Allocators:

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