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Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations....

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Lukas Ziegler

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59,708 次观看 • 7 个月前 •via X (Twitter)

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TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.
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TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

The Humanoid Hub

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Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.
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Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Reborn

95,499 次观看 • 11 个月前

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.
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A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

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

CyberRobo

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A planetary roller screw looks simple, but it’s one of the key enablers for humanoid robots. It converts rotational motion into precise linear motion with high efficiency, high load capacity, and extremely smooth control. In humanoid robots, this is exactly what you need for: joint actuation linear actuators in arms/legs high-force, high-precision movement under continuous cycles Compared to ball screws, it offers better durability and load distribution—critical for robots that must operate 24/7 in the real world. Small component, huge impact. This is the kind of “hidden hardware” that makes humanoid robotics actually work.
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A planetary roller screw looks simple, but it’s one of the key enablers for humanoid robots. It converts rotational motion into precise linear motion with high efficiency, high load capacity, and extremely smooth control. In humanoid robots, this is exactly what you need for: joint actuation linear actuators in arms/legs high-force, high-precision movement under continuous cycles Compared to ball screws, it offers better durability and load distribution—critical for robots that must operate 24/7 in the real world. Small component, huge impact. This is the kind of “hidden hardware” that makes humanoid robotics actually work.

Steven Cheng

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Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:
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Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:

RoboHub🤖

61,378 次观看 • 10 个月前

A cute humanoid robot from NYC Fauna Robotics has released a humanoid robot called Sprout, which is approximately 1 meter tall, lightweight, quiet, and features no dangerous edges and soft skin. Its appearance and interactions are designed to be "approachable," like a social companion. It is currently offered as a general-purpose robotics platform for developers, providing basic movement, perception, navigation, and expression capabilities, allowing real-world use cases to emerge within the community. Many humanoid robots are born in factories and warehouses, but Fauna Robotics aims to bring humanoid robots back into human living spaces. Fauna believes that if humanoid robots are to become as ubiquitous as computers and mobile phones in the future, the starting point must be a safe, friendly form suitable for homes, schools, and offices. Sprout is their first step in this direction.
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A cute humanoid robot from NYC Fauna Robotics has released a humanoid robot called Sprout, which is approximately 1 meter tall, lightweight, quiet, and features no dangerous edges and soft skin. Its appearance and interactions are designed to be "approachable," like a social companion. It is currently offered as a general-purpose robotics platform for developers, providing basic movement, perception, navigation, and expression capabilities, allowing real-world use cases to emerge within the community. Many humanoid robots are born in factories and warehouses, but Fauna Robotics aims to bring humanoid robots back into human living spaces. Fauna believes that if humanoid robots are to become as ubiquitous as computers and mobile phones in the future, the starting point must be a safe, friendly form suitable for homes, schools, and offices. Sprout is their first step in this direction.

CyberRobo

18,549 次观看 • 6 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.
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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

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

Mario Nawfal

89,193 次观看 • 1 年前

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

Lukas Ziegler

50,063 次观看 • 7 个月前

Yann LeCun says today's humanoid robot demos look impressive, but the robots are very stupid in the real world "the missing piece is not hardware; it is AI that can't reason, plan, or adapt like humans" Robot companies are betting AI will make them smart enough to sell at scale within 3-5 years It's a big bet
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Yann LeCun says today's humanoid robot demos look impressive, but the robots are very stupid in the real world "the missing piece is not hardware; it is AI that can't reason, plan, or adapt like humans" Robot companies are betting AI will make them smart enough to sell at scale within 3-5 years It's a big bet

Haider.

37,260 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 →
0:56

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 次观看 • 5 个月前

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

ELON CLIPS

23,613 次观看 • 11 个月前

Jim Fan says NVIDIA trained humanoid robots to walk and move like humans -- zero-shot transfer from simulation to the real world. 10 years of learning, compressed into 2 hours of simulation. Turns out you don’t need a giant model to master motion. “1.5 million parameters, not billion, to capture the subconscious processing of the human body.” Big embodiment, small brain.
1:26

Jim Fan says NVIDIA trained humanoid robots to walk and move like humans -- zero-shot transfer from simulation to the real world. 10 years of learning, compressed into 2 hours of simulation. Turns out you don’t need a giant model to master motion. “1.5 million parameters, not billion, to capture the subconscious processing of the human body.” Big embodiment, small brain.

vitrupo

714,741 次观看 • 1 年前

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
0:29

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,864 次观看 • 6 个月前

We RL'ed humanoid robots to Cristiano Ronaldo, LeBron James, and Kobe Byrant! These are neural nets running on real hardware at our GEAR lab. Most robot demos you see online speed videos up. We actually *slow them down* so you can enjoy the fluid motions. I'm excited to announce "ASAP", a "real2sim2real" model that masters extremely smooth and dynamic motions for humanoid whole body control. We pretrain the robot in simulation first, but there is a notorious "sim2real" gap: it's very difficult for hand-engineered physics equations to match real world dynamics. Our fix is simple: just deploy a pretrained policy on real hardware, collect data, and replay the motion in sim. The replay will obviously have many errors, but that gives a rich signal to compensate for the physics discrepancy. Use another neural net to learn the delta. Basically, we "patch up" a traditional physics engine, so that the robot can experience almost the real world at scale in GPUs. The future is hybrid simulation: combine the power of classical sim engines refined over decades and the uncanny ability of modern NNs to capture a messy world.
0:50

We RL'ed humanoid robots to Cristiano Ronaldo, LeBron James, and Kobe Byrant! These are neural nets running on real hardware at our GEAR lab. Most robot demos you see online speed videos up. We actually *slow them down* so you can enjoy the fluid motions. I'm excited to announce "ASAP", a "real2sim2real" model that masters extremely smooth and dynamic motions for humanoid whole body control. We pretrain the robot in simulation first, but there is a notorious "sim2real" gap: it's very difficult for hand-engineered physics equations to match real world dynamics. Our fix is simple: just deploy a pretrained policy on real hardware, collect data, and replay the motion in sim. The replay will obviously have many errors, but that gives a rich signal to compensate for the physics discrepancy. Use another neural net to learn the delta. Basically, we "patch up" a traditional physics engine, so that the robot can experience almost the real world at scale in GPUs. The future is hybrid simulation: combine the power of classical sim engines refined over decades and the uncanny ability of modern NNs to capture a messy world.

Jim Fan

566,544 次观看 • 1 年前

John: "Over in China, they’re doing robot Olympics. They’re doing marathons and all sorts of stuff." Figure founder Brett Adcock: “We look at the distribution of what humans do that is useful, and we [designed our humanoid] to do as much of that as possible.” “These things where we run marathons, or we do backflips, or we do karate moves—they’re not in the main part of the distribution. They don’t matter. And if you really want to size for those, you’re going to build a really expensive and unsafe robot that’s hard to manufacture.” “People want the $10,000 humanoid that can do general-purpose work. We want it to do laundry and dishes and be a companion. I want to ship robots at scale into the workforce to do logistics and healthcare, and build buildings and data centers.” "I don't need [our humanoid] to do backflips to do any of that work. I want robots building other robots."
2:14

John: "Over in China, they’re doing robot Olympics. They’re doing marathons and all sorts of stuff." Figure founder Brett Adcock: “We look at the distribution of what humans do that is useful, and we [designed our humanoid] to do as much of that as possible.” “These things where we run marathons, or we do backflips, or we do karate moves—they’re not in the main part of the distribution. They don’t matter. And if you really want to size for those, you’re going to build a really expensive and unsafe robot that’s hard to manufacture.” “People want the $10,000 humanoid that can do general-purpose work. We want it to do laundry and dishes and be a companion. I want to ship robots at scale into the workforce to do logistics and healthcare, and build buildings and data centers.” "I don't need [our humanoid] to do backflips to do any of that work. I want robots building other robots."

TBPN

15,419 次观看 • 5 个月前

.Elon Musk told me he plans to build “Optimus Academy” to train an army of humanoid robots. > “millions of simulated robots in the simulated world” > “tens of thousands of robots in the real world, to close the simulation to reality gap.” Why? There’s two big differences between the way Tesla will train humanoid robots and the way it built Full Self-Driving. - FSD only requires learning three degrees of freedom: turning, accelerating, braking … and a humanoid robot has to learn how to coordinate 50+ joints. - You can sell a Tesla without FSD, which allows you to collect the millions and millions of hours of driving data needed to bootstrap your model ... but you can’t sell an Optimus that hasn’t learned how to do stuff yet. The Optimus Academy is Elon’s attempt to build the data flywheel that Tesla got for free with FSD.
2:10

.Elon Musk told me he plans to build “Optimus Academy” to train an army of humanoid robots. > “millions of simulated robots in the simulated world” > “tens of thousands of robots in the real world, to close the simulation to reality gap.” Why? There’s two big differences between the way Tesla will train humanoid robots and the way it built Full Self-Driving. - FSD only requires learning three degrees of freedom: turning, accelerating, braking … and a humanoid robot has to learn how to coordinate 50+ joints. - You can sell a Tesla without FSD, which allows you to collect the millions and millions of hours of driving data needed to bootstrap your model ... but you can’t sell an Optimus that hasn’t learned how to do stuff yet. The Optimus Academy is Elon’s attempt to build the data flywheel that Tesla got for free with FSD.

Dwarkesh Patel

539,948 次观看 • 5 个月前

🤯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.
2:34

🤯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

67,287 次观看 • 7 个月前

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
2:18

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 次观看 • 5 个月前