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Dodging randomly thrown balls doesn’t seem that difficult… Shanghai-based EmbodyDeep tested their CereBase 3.0, a powerful real-time motion model, on the Unitree G1. The robot dodges incoming soccer balls with lifelike reflexes: instantly detecting the ball, predicting its trajectory, and smoothly stepping, twisting, or crouching while staying perfectly balanced.... show more

CyberRobo

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20,684 views • 2 months ago •via X (Twitter)

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The humanoid robot Lingxi X2 can easily dodge thrown balls… powered by AGIBOT’s newly launched AGILE perception-motion foundation model. With this motion intelligence, it can avoid interference and make real-time perceptual adjustments when working in complex environments. I’m curious whether it could dodge a human’s flying kick? (They always seem to kick humanoid robots during testing.)

The humanoid robot Lingxi X2 can easily dodge thrown balls… powered by AGIBOT’s newly launched AGILE perception-motion foundation model. With this motion intelligence, it can avoid interference and make real-time perceptual adjustments when working in complex environments. I’m curious whether it could dodge a human’s flying kick? (They always seem to kick humanoid robots during testing.)

CyberRobo

32,796 views • 2 months ago

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 views • 10 months ago

People still say humanoid robots cannot handle creativity, rhythm, or coordinated motion. Meanwhile, a Unitree humanoid is already playing drums with timing, arm synchronization, balance control, and real time motion planning. Here is why this matters: Goldman Sachs projects the humanoid robot market could reach $38 billion by 2035, with shipments around 1.4 million units. Unitree’s R1 started around $5,600, while its earlier G1 was around $16,000, showing how fast humanoid hardware costs are falling. A drum performance may look like entertainment, but it tests the same foundations robotics needs everywhere: motion control latency balance precision spatial awareness task repetition A robot does not need to become human to start replacing human tasks. It only needs to become good enough. And that line is getting closer every year.

People still say humanoid robots cannot handle creativity, rhythm, or coordinated motion. Meanwhile, a Unitree humanoid is already playing drums with timing, arm synchronization, balance control, and real time motion planning. Here is why this matters: Goldman Sachs projects the humanoid robot market could reach $38 billion by 2035, with shipments around 1.4 million units. Unitree’s R1 started around $5,600, while its earlier G1 was around $16,000, showing how fast humanoid hardware costs are falling. A drum performance may look like entertainment, but it tests the same foundations robotics needs everywhere: motion control latency balance precision spatial awareness task repetition A robot does not need to become human to start replacing human tasks. It only needs to become good enough. And that line is getting closer every year.

Techniahqrobot | humanoid robots

22,657 views • 3 months ago

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

20,898 views • 6 months ago

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

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

Lukas Ziegler

59,835 views • 7 months ago

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 views • 5 months ago

Humanoid robot convenience stores are becoming a part of people's lives. A couple of days ago, I passed by the Zhangjiang AI Town in Shanghai. At the GalBOT "Space Capsule," I bought a bottle of Coca-Cola from a convenience store operated by a humanoid robot. It was smoother than I expected: screen selection, QR code payment, and the humanoid robot G1 retrieving and delivering the goods – all in one seamless process. It's worth noting that a human worker was still present inside, handling emergency situations. However, it can't yet take orders through speech, but this will change soon. People will be able to shop by naturally conversing with G1, paying with palm or facial recognition (e.g., Alipay), and the humanoid robot will autonomously fulfill orders without ever getting tired, always full of enthusiasm and patience. It is expected that convenience stores operated by humanoid robots will soon appear in the core areas and tourist zones of many Chinese cities this year, with companies like GalBOT, Astribot, and AI² Robotics deploying them in large numbers. Its value goes beyond simply achieving intelligent automation in retail; it's becoming an integral part of people's future technological lives.

Humanoid robot convenience stores are becoming a part of people's lives. A couple of days ago, I passed by the Zhangjiang AI Town in Shanghai. At the GalBOT "Space Capsule," I bought a bottle of Coca-Cola from a convenience store operated by a humanoid robot. It was smoother than I expected: screen selection, QR code payment, and the humanoid robot G1 retrieving and delivering the goods – all in one seamless process. It's worth noting that a human worker was still present inside, handling emergency situations. However, it can't yet take orders through speech, but this will change soon. People will be able to shop by naturally conversing with G1, paying with palm or facial recognition (e.g., Alipay), and the humanoid robot will autonomously fulfill orders without ever getting tired, always full of enthusiasm and patience. It is expected that convenience stores operated by humanoid robots will soon appear in the core areas and tourist zones of many Chinese cities this year, with companies like GalBOT, Astribot, and AI² Robotics deploying them in large numbers. Its value goes beyond simply achieving intelligent automation in retail; it's becoming an integral part of people's future technological lives.

CyberRobo

42,073 views • 7 months ago

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

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

Eren Chen

10,350 views • 1 month ago

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,472 views • 1 year ago

No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.

No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.

CyberRobo

22,118 views • 4 months ago

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Chidanand Tripathi

69,604 views • 5 months ago

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

165,246 views • 1 year ago

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 views • 1 year ago

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

Rohan Paul

52,996 views • 4 months ago

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

68,453 views • 7 months ago

🤯incredible,its playability and usability are outstanding. Tron2 is a LEGO robot. It can be assembled into various forms depending on the scenario, including bipedal, wheeled, and humanoid torso configurations, and its end effectors are also interchangeable. Each form of the robot is driven by a VLA model. It can perform complex movements and can also be integrated into existing workflows to perform tasks. It's worth mentioning that this solves the problem of which type of humanoid robot to use. Each user can assemble the robot in a configuration that suits their specific scenario, making it truly flexible and versatile – it doesn't necessarily have to be bipedal or wheeled... I look forward to seeing more real-world use cases.

🤯incredible,its playability and usability are outstanding. Tron2 is a LEGO robot. It can be assembled into various forms depending on the scenario, including bipedal, wheeled, and humanoid torso configurations, and its end effectors are also interchangeable. Each form of the robot is driven by a VLA model. It can perform complex movements and can also be integrated into existing workflows to perform tasks. It's worth mentioning that this solves the problem of which type of humanoid robot to use. Each user can assemble the robot in a configuration that suits their specific scenario, making it truly flexible and versatile – it doesn't necessarily have to be bipedal or wheeled... I look forward to seeing more real-world use cases.

CyberRobo

147,344 views • 7 months ago

Alarming New Video Shows Robot Making Incredibly Realistic Facial Expressions | Victor Tangermann, The_Byte A robotics company in China has shown off a humanoid robotic head that can express emotions through extremely subtle movements of its facial features. A video that has gone viral on social media shows the face glancing around the room with a quizzical expression. Its eyes blink in an eerily lifelike way, selling the illusion surprisingly well. Hangzhou, China-based outfit AheadForm, which is behind the impressive demo, claims on its website to combine "self-supervised AI algorithms" and wide-range "bionic actuation" to "express authentic emotions and lifelike facial expressions." The demo goes to show how far human mimicry in the field of humanoid robotics has come, landing in the furthest reaches of the uncanny valley. "Well... 'Westworld' is closer than I thought," one Reddit user pondered in response to the video, referring to the hit HBO sci-fi show — and 1973 movie, and Michael Crichton novel — about a fictional amusement park populated by android "hosts." AheadForm says it's already developed a series of "ultra-lifelike" humanoid "elves," which can "perceive the world, communicate, learn, and interact intelligently with its surroundings" thanks to an "advanced AI learning algorithm." According to the company's founder, Hu Yuhang, the lines between human and robot are bound to continue to blur, eventually becoming barely distinguishable from each other. "Within ten years, we might interact with robots and feel like they are almost human; maybe in 20 years, they could walk normally and perform some tasks just like a human," he told the South China Morning Post last year. For now, the humanoid robot industry's main focus is productivity, rather than mimicking human personality expressions. A growing cohort of companies, including Tesla, is aiming to replace human labor with their offerings, teach them how to kickbox, and selling them as the ultimate household aid. However, as Yuhang suggests, such a future appears to still be a long way out. Not everybody's convinced that bipedal robots are the answer, either, with some arguing that purpose-built industrial robots will always be a better option. In the meantime, AheadForm aims to prepare us for an android-filled future by using AI to make human-robot interactions more "natural and engaging." But given their robots' disconcertingly probing eyes, that won't be an easy task.

Alarming New Video Shows Robot Making Incredibly Realistic Facial Expressions | Victor Tangermann, The_Byte A robotics company in China has shown off a humanoid robotic head that can express emotions through extremely subtle movements of its facial features. A video that has gone viral on social media shows the face glancing around the room with a quizzical expression. Its eyes blink in an eerily lifelike way, selling the illusion surprisingly well. Hangzhou, China-based outfit AheadForm, which is behind the impressive demo, claims on its website to combine "self-supervised AI algorithms" and wide-range "bionic actuation" to "express authentic emotions and lifelike facial expressions." The demo goes to show how far human mimicry in the field of humanoid robotics has come, landing in the furthest reaches of the uncanny valley. "Well... 'Westworld' is closer than I thought," one Reddit user pondered in response to the video, referring to the hit HBO sci-fi show — and 1973 movie, and Michael Crichton novel — about a fictional amusement park populated by android "hosts." AheadForm says it's already developed a series of "ultra-lifelike" humanoid "elves," which can "perceive the world, communicate, learn, and interact intelligently with its surroundings" thanks to an "advanced AI learning algorithm." According to the company's founder, Hu Yuhang, the lines between human and robot are bound to continue to blur, eventually becoming barely distinguishable from each other. "Within ten years, we might interact with robots and feel like they are almost human; maybe in 20 years, they could walk normally and perform some tasks just like a human," he told the South China Morning Post last year. For now, the humanoid robot industry's main focus is productivity, rather than mimicking human personality expressions. A growing cohort of companies, including Tesla, is aiming to replace human labor with their offerings, teach them how to kickbox, and selling them as the ultimate household aid. However, as Yuhang suggests, such a future appears to still be a long way out. Not everybody's convinced that bipedal robots are the answer, either, with some arguing that purpose-built industrial robots will always be a better option. In the meantime, AheadForm aims to prepare us for an android-filled future by using AI to make human-robot interactions more "natural and engaging." But given their robots' disconcertingly probing eyes, that won't be an easy task.

Owen Gregorian

105,829 views • 10 months ago

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

RoboHub🤖

10,952 views • 4 months ago

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

Ashish Kapoor

118,133 views • 10 months ago