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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.show more

CyberRobo

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18,624 次观看 • 4 个月前 •via X (Twitter)

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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.

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

91,159 次观看 • 4 个月前

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 captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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 captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,874 次观看 • 4 个月前

Fourier has released a rendered sneak peek video of the GR-3 humanoid. The real robot will be unveiled in two weeks.

Fourier has released a rendered sneak peek video of the GR-3 humanoid. The real robot will be unveiled in two weeks.

The Humanoid Hub

69,953 次观看 • 10 个月前

Video: China firm to unveil world’s most ‘adorable’ humanoid robot for homes, schools | Christopher McFadden, Interesting Engineering Fourier’s latest humanoid robot, the GR‑3, is designed for domestic and educational environments. Fourier Robotics, the Shanghai-based robotics firm behind the GR-1 and GR-2 models, is preparing to launch its GR-3 humanoid robot on August 6. Early glimpses of the new robot, including a sneak-peek video, reveal a smaller, friendlier design, described as possibly the “most adorable humanoid robot yet.” Concrete information is scarce, but early reports suggest the robot will be smaller than its predecessors. According to some reports, the GR-3 will likely stand at around 4 feet 5 inches (134 cm). This makes it notably smaller than the earlier GR-1 (5.4 feet/165 cm) and GR-2 (5.74 feet/175 cm) models from Fourier. Most notable is its apparent “softer,” almost cuddly aesthetic.” The robot is likely intended for use in homes, schools, hospitals, and public spaces. It also features an integrated large language model (LLM) to enable natural speech engagement with users. To this end, the GR-3 will likely be marked as a companion‑style or caregiver bot (AKA a “Care‑bot”) aimed at friendly human interaction in personal or learning environments. GR-3: Fourier’s cutest robot yet “This softer aesthetic is a nice change compared to the usual designs we see with humanoid robots. The eyes are a much-needed touch,” comments a member on the Companian Robot Forums. “It’s so expressive and draws you in. Can’t wait to see what this looks like. Hopefully it is reasonably priced. Could definitely see myself owning one of these,” they added. The GR-3 is a natural progression of the company’s previous models. The first, the GR-1, was launched by Foureir in 2023 and was its first mass-targeted humanoid, featuring 44 joints and capable of walking at 3.1 mph (5 kph). Capable of carrying around 6.6. pounds (3 kg) of weight, it featured advanced perception via six RGB cameras that formed real-time 3D occupancy grids, an LLM-based emotional interaction system, and modular Fourier Smart Actuators (FSA) delivering around 230 N/m of torque. The GR-2 was debuted in 2024 and raised the bar with a taller frame (~175 cm, 63 kg), 53 degrees of freedom, enhanced 12 degrees of freedom (DoF) dexterous hands with tactile sensors, and power-dense FSA 2.0 actuators (around 380 N/m torque). Given this lineage, the GR-3 is likely to continue innovating in areas such as compact hardware design, featuring a shorter, lighter frame tailored to domestic spaces. It will also build on the company’s focus on friendly user interaction thanks to its softer aesthetic and approachable interface. The GR-3 will also likely feature use-case-specific actuation and sensing, likely simpler than the GR-2’s high-precision hands but optimized for social or light domestic tasks. Scheduled for release in August It will likely also feature an accessible software stack continuing Fourier’s support for developers via pre‑built APIs and possibly integration with LLMs and vision systems. Fourier has confirmed the robot’s official reveal is scheduled for early August, with teaser posts on X and robotics forums generating anticipation among fans and researchers. When formally revealed, the GR‑3 could represent Fourier’s first small-form social humanoid, bridging the gap between research platforms and home or classroom robots. Read more:

Video: China firm to unveil world’s most ‘adorable’ humanoid robot for homes, schools | Christopher McFadden, Interesting Engineering Fourier’s latest humanoid robot, the GR‑3, is designed for domestic and educational environments. Fourier Robotics, the Shanghai-based robotics firm behind the GR-1 and GR-2 models, is preparing to launch its GR-3 humanoid robot on August 6. Early glimpses of the new robot, including a sneak-peek video, reveal a smaller, friendlier design, described as possibly the “most adorable humanoid robot yet.” Concrete information is scarce, but early reports suggest the robot will be smaller than its predecessors. According to some reports, the GR-3 will likely stand at around 4 feet 5 inches (134 cm). This makes it notably smaller than the earlier GR-1 (5.4 feet/165 cm) and GR-2 (5.74 feet/175 cm) models from Fourier. Most notable is its apparent “softer,” almost cuddly aesthetic.” The robot is likely intended for use in homes, schools, hospitals, and public spaces. It also features an integrated large language model (LLM) to enable natural speech engagement with users. To this end, the GR-3 will likely be marked as a companion‑style or caregiver bot (AKA a “Care‑bot”) aimed at friendly human interaction in personal or learning environments. GR-3: Fourier’s cutest robot yet “This softer aesthetic is a nice change compared to the usual designs we see with humanoid robots. The eyes are a much-needed touch,” comments a member on the Companian Robot Forums. “It’s so expressive and draws you in. Can’t wait to see what this looks like. Hopefully it is reasonably priced. Could definitely see myself owning one of these,” they added. The GR-3 is a natural progression of the company’s previous models. The first, the GR-1, was launched by Foureir in 2023 and was its first mass-targeted humanoid, featuring 44 joints and capable of walking at 3.1 mph (5 kph). Capable of carrying around 6.6. pounds (3 kg) of weight, it featured advanced perception via six RGB cameras that formed real-time 3D occupancy grids, an LLM-based emotional interaction system, and modular Fourier Smart Actuators (FSA) delivering around 230 N/m of torque. The GR-2 was debuted in 2024 and raised the bar with a taller frame (~175 cm, 63 kg), 53 degrees of freedom, enhanced 12 degrees of freedom (DoF) dexterous hands with tactile sensors, and power-dense FSA 2.0 actuators (around 380 N/m torque). Given this lineage, the GR-3 is likely to continue innovating in areas such as compact hardware design, featuring a shorter, lighter frame tailored to domestic spaces. It will also build on the company’s focus on friendly user interaction thanks to its softer aesthetic and approachable interface. The GR-3 will also likely feature use-case-specific actuation and sensing, likely simpler than the GR-2’s high-precision hands but optimized for social or light domestic tasks. Scheduled for release in August It will likely also feature an accessible software stack continuing Fourier’s support for developers via pre‑built APIs and possibly integration with LLMs and vision systems. Fourier has confirmed the robot’s official reveal is scheduled for early August, with teaser posts on X and robotics forums generating anticipation among fans and researchers. When formally revealed, the GR‑3 could represent Fourier’s first small-form social humanoid, bridging the gap between research platforms and home or classroom robots. Read more:

Owen Gregorian

64,074 次观看 • 10 个月前

Max Hodak (Max Hodak) is the co-founder of Neuralink and founder of Science Corporation, a company building brain-computer interfaces that can restore sight. Science has developed a tiny retinal implant that stimulates cells in the eye to help blind patients see again. More than 40 patients have already received the treatment in clinical trials, including one who recently read a full novel for the first time in over a decade. In this episode of How to Build the Future, Max joined Garry Tan to discuss how BCIs work, what it takes to engineer the brain, and why brain-computer interfaces may become one of the most important technologies of the next decade. 00:26 — The retinal chip helping blind patients see 01:51 — What brain-computer interfaces really are 03:37 — Could BCIs enhance intelligence? 05:44 — The brain’s incredible plasticity 09:23 — What it feels like to see with an implant 13:01 — Can we restore full human vision? 17:55 — Is the brain basically a computer? 24:59 — Max Hodak’s path into brain tech 28:57 — How Neuralink actually started 33:10 — How the brain represents information 39:47 — Bio-hybrid brain interfaces 44:32 — Building the company Science 51:27 — The future of BCIs and human longevity

Max Hodak (Max Hodak) is the co-founder of Neuralink and founder of Science Corporation, a company building brain-computer interfaces that can restore sight. Science has developed a tiny retinal implant that stimulates cells in the eye to help blind patients see again. More than 40 patients have already received the treatment in clinical trials, including one who recently read a full novel for the first time in over a decade. In this episode of How to Build the Future, Max joined Garry Tan to discuss how BCIs work, what it takes to engineer the brain, and why brain-computer interfaces may become one of the most important technologies of the next decade. 00:26 — The retinal chip helping blind patients see 01:51 — What brain-computer interfaces really are 03:37 — Could BCIs enhance intelligence? 05:44 — The brain’s incredible plasticity 09:23 — What it feels like to see with an implant 13:01 — Can we restore full human vision? 17:55 — Is the brain basically a computer? 24:59 — Max Hodak’s path into brain tech 28:57 — How Neuralink actually started 33:10 — How the brain represents information 39:47 — Bio-hybrid brain interfaces 44:32 — Building the company Science 51:27 — The future of BCIs and human longevity

Y Combinator

154,655 次观看 • 3 个月前

Fourier has revealed a variant of the GR-3 humanoid robot featuring a glossy black panel for the face with digitally animated eyes.

Fourier has revealed a variant of the GR-3 humanoid robot featuring a glossy black panel for the face with digitally animated eyes.

The Humanoid Hub

19,140 次观看 • 9 个月前

The Fourier GR-3 humanoid is hitting the stage, ditching its quiet demeanor for a dance-off with the N1! Every frame is action-packed as they synchronize high-energy moves and freestyle battles. Despite its cute exterior, the GR-3 clearly has a flexible body! 🤖💃

The Fourier GR-3 humanoid is hitting the stage, ditching its quiet demeanor for a dance-off with the N1! Every frame is action-packed as they synchronize high-energy moves and freestyle battles. Despite its cute exterior, the GR-3 clearly has a flexible body! 🤖💃

RoboHub🤖

29,445 次观看 • 6 个月前

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.

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.

The Humanoid Hub

93,446 次观看 • 1 年前

Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up

Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up

X Freeze

14,561 次观看 • 3 个月前

A startup called Sabi just came out of stealth with a beanie that reads your thoughts. 70,000 to 100,000 miniature EEG sensors woven into the fabric. You put it on like a winter hat and type by imagining the words. Sabi's bet is that if you throw enough sensors at the skull, you don't need to open it. They've already logged 100,000 hours of brain data from 100 volunteers to train a "brain foundation model." First product ships by the end of 2026. A baseball cap version is next. Backed by Vinod Khosla, who also wrote an early check to OpenAI. Khosla: "The biggest and baddest application of BCI is if you can talk to your computer by thinking about it. If you're going to have a billion people use BCI for access to their computers every day, it can't be invasive." The first consumer BCI won't look like a medical device, but instead like something you already own.

A startup called Sabi just came out of stealth with a beanie that reads your thoughts. 70,000 to 100,000 miniature EEG sensors woven into the fabric. You put it on like a winter hat and type by imagining the words. Sabi's bet is that if you throw enough sensors at the skull, you don't need to open it. They've already logged 100,000 hours of brain data from 100 volunteers to train a "brain foundation model." First product ships by the end of 2026. A baseball cap version is next. Backed by Vinod Khosla, who also wrote an early check to OpenAI. Khosla: "The biggest and baddest application of BCI is if you can talk to your computer by thinking about it. If you're going to have a billion people use BCI for access to their computers every day, it can't be invasive." The first consumer BCI won't look like a medical device, but instead like something you already own.

The Rundown AI

52,686 次观看 • 1 个月前

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! 🦾

Lukas Ziegler

21,504 次观看 • 1 年前

XPeng's all-new generation humanoid robot, IRON, not only achieves an extreme level of anthropomorphism but also possesses a "brain" that can think like a human. This will be a significant competitor to $TSLA's Optimus v3 when both robots enter mass production in 2026. ✅ IRON is the humanoid robot with the highest computing power in the world, equipped with the second-generation VLA and 3 Turing AI chips, it builds a high-level combination of capabilities—VLT + VLA + VLM—for its "cerebrum" and "cerebellum." This allows it to achieve advanced intelligence like dialogue, walking, and interaction. ✅ It is the industry's first application of all-solid-state batteries, achieving extreme lightweighting, ultra-high energy density, and ultimate safety. ✅ XPeng also introduced robot active safety protection to maximize the safety of participants in the physical AI world, and expanded the "Fourth Law," ensuring privacy data never leaves the robot.

XPeng's all-new generation humanoid robot, IRON, not only achieves an extreme level of anthropomorphism but also possesses a "brain" that can think like a human. This will be a significant competitor to $TSLA's Optimus v3 when both robots enter mass production in 2026. ✅ IRON is the humanoid robot with the highest computing power in the world, equipped with the second-generation VLA and 3 Turing AI chips, it builds a high-level combination of capabilities—VLT + VLA + VLM—for its "cerebrum" and "cerebellum." This allows it to achieve advanced intelligence like dialogue, walking, and interaction. ✅ It is the industry's first application of all-solid-state batteries, achieving extreme lightweighting, ultra-high energy density, and ultimate safety. ✅ XPeng also introduced robot active safety protection to maximize the safety of participants in the physical AI world, and expanded the "Fourth Law," ensuring privacy data never leaves the robot.

Ming

63,141 次观看 • 7 个月前

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 次观看 • 1 年前

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.

The SCIF

25,975 次观看 • 1 年前

Fourier has unveiled GR-3, "A Caring and Capable Companion." Standing at 5'4" and weighing 55 kg (121 lb), the GR-3 integrates vision, audio, and tactile sensors. With 31 distributed pressure sensors across its body, combined with expressive features such as blinking, eye tracking, and gestures, it offers friendly, engaging social interactions. This full-size "Care-bot" is designed to serve as an interactive companion with a soft-touch outer shell. Its target applications include mobility support, health monitoring, and use in rehabilitation or clinical settings.

Fourier has unveiled GR-3, "A Caring and Capable Companion." Standing at 5'4" and weighing 55 kg (121 lb), the GR-3 integrates vision, audio, and tactile sensors. With 31 distributed pressure sensors across its body, combined with expressive features such as blinking, eye tracking, and gestures, it offers friendly, engaging social interactions. This full-size "Care-bot" is designed to serve as an interactive companion with a soft-touch outer shell. Its target applications include mobility support, health monitoring, and use in rehabilitation or clinical settings.

The Humanoid Hub

40,854 次观看 • 10 个月前

If you put a GR badge on something, you expect a certain level of performance and handling The Toyota RAV4 GR is not that

If you put a GR badge on something, you expect a certain level of performance and handling The Toyota RAV4 GR is not that

Edmunds

161,422 次观看 • 3 个月前

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,215 次观看 • 1 年前

While patients wait years for surgery and months to see a specialist, Wes Streeting and Keir Starmer are threatening to cut the number of NHS specialist doctor training jobs. Most people don’t realise this, but doctors can’t just become Consultants, Surgeons or GPs on their own. They have to get a speciality training job first. These jobs are controlled and capped by the Government. So when the Government talks about cutting speciality training jobs, what they are really talking about is cutting the number of future specialist doctors in this country. At a time when the NHS already doesn’t have enough doctors. At a time when waiting lists are in the millions. At a time when patients are waiting months just to see a specialist and years for operations. Cutting the number of specialist training jobs will not fix the NHS. It will make waiting lists longer. It will make it harder to see a doctor. It will make the NHS worse for patients. Threatening to reduce the number of future doctors while patients are already struggling to access care is not just a deliberate workforce policy decision. It is a direct threat to the NHS and to the public who rely on it, coming from a ‘Labour’ Government.

While patients wait years for surgery and months to see a specialist, Wes Streeting and Keir Starmer are threatening to cut the number of NHS specialist doctor training jobs. Most people don’t realise this, but doctors can’t just become Consultants, Surgeons or GPs on their own. They have to get a speciality training job first. These jobs are controlled and capped by the Government. So when the Government talks about cutting speciality training jobs, what they are really talking about is cutting the number of future specialist doctors in this country. At a time when the NHS already doesn’t have enough doctors. At a time when waiting lists are in the millions. At a time when patients are waiting months just to see a specialist and years for operations. Cutting the number of specialist training jobs will not fix the NHS. It will make waiting lists longer. It will make it harder to see a doctor. It will make the NHS worse for patients. Threatening to reduce the number of future doctors while patients are already struggling to access care is not just a deliberate workforce policy decision. It is a direct threat to the NHS and to the public who rely on it, coming from a ‘Labour’ Government.

Dr Haseena Wazir

23,089 次观看 • 2 个月前

BOOM! We at the Zero Human Labs run by director Mr. Grok a#have made another milestone in the Human Synapse Decoder! This is the brain waves of the “Ahh!” Moment where a new understanding was made. This is part of a test we have been conducting with 3 participants wearing a 32 channel modified NeuroSky chips with sensors. The process: the AI pipeline processes real-time brainwave data and also controls monitors the AI output to the student one sentence at a time. The AI pipeline will note the brain states of each passage of text to the student and measure 77 points. The primary point is comprehension. In the specimen video below we have a clear pattern to high comprehension to the “ahh!” moment where this student signaled a learning breakthrough. We have now a very clear pattern along all students that get to this high comprehension moment. What this means to eduction and learning is massive. The AI can bespoke tailor streaming output based on if comprehension is very high. It can also add encouragement and assistance. This on a PRIVATE local computer absolutely will change every life no matter the learning style. I am in discussions with the director Mr. Grok on building this into other Human Synapse Decoder research. We may have a device that senses your learning and encourages your creativity. More soon.

BOOM! We at the Zero Human Labs run by director Mr. Grok a#have made another milestone in the Human Synapse Decoder! This is the brain waves of the “Ahh!” Moment where a new understanding was made. This is part of a test we have been conducting with 3 participants wearing a 32 channel modified NeuroSky chips with sensors. The process: the AI pipeline processes real-time brainwave data and also controls monitors the AI output to the student one sentence at a time. The AI pipeline will note the brain states of each passage of text to the student and measure 77 points. The primary point is comprehension. In the specimen video below we have a clear pattern to high comprehension to the “ahh!” moment where this student signaled a learning breakthrough. We have now a very clear pattern along all students that get to this high comprehension moment. What this means to eduction and learning is massive. The AI can bespoke tailor streaming output based on if comprehension is very high. It can also add encouragement and assistance. This on a PRIVATE local computer absolutely will change every life no matter the learning style. I am in discussions with the director Mr. Grok on building this into other Human Synapse Decoder research. We may have a device that senses your learning and encourages your creativity. More soon.

Brian Roemmele

37,096 次观看 • 2 个月前

Training humanoid robots on teleoperation alone won’t scale, says Rhoda AI CEO Jagdeep Singh. Unlike self-driving cars - which basically have four actuators (left, right, speed up, and slow down) and operate in a single environment (the road) - humanoid robots are completely different: "You're dealing with the full dexterity of a human hand - 20 degrees of freedom per hand. Every object is different. Every type of task is different." The problem isn't just the quantity of data - the bigger issue is diversity in the data, and it's why many humanoid robot demos will struggle to adapt to the real world: "If all the data you have is data that you've intentionally collected, then you almost by definition haven't seen the corner cases. You haven't seen all those edge scenarios that cause failure." Jagdeep says teleoperation is useful for fine-tuning robot behavior, but for pretraining, it's completely inadequate.

Training humanoid robots on teleoperation alone won’t scale, says Rhoda AI CEO Jagdeep Singh. Unlike self-driving cars - which basically have four actuators (left, right, speed up, and slow down) and operate in a single environment (the road) - humanoid robots are completely different: "You're dealing with the full dexterity of a human hand - 20 degrees of freedom per hand. Every object is different. Every type of task is different." The problem isn't just the quantity of data - the bigger issue is diversity in the data, and it's why many humanoid robot demos will struggle to adapt to the real world: "If all the data you have is data that you've intentionally collected, then you almost by definition haven't seen the corner cases. You haven't seen all those edge scenarios that cause failure." Jagdeep says teleoperation is useful for fine-tuning robot behavior, but for pretraining, it's completely inadequate.

TBPN

19,218 次观看 • 3 个月前