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This Unitree G1 performed surgery with teleoperation. First successful surgery with a humanoid. A surgeon steered it from a console the whole time. It used real surgical tools, not custom robotic ones. The G1 completed two live gallbladder removals on pigs. The work just published in Nature.

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64,655 次观看 • 1 个月前 •via X (Twitter)

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A humanoid robot doing gallbladder surgery sounds fake until you watch the instruments move. This ARCLab / UC San Diego demo shows teleoperated humanoids working through a cholecystectomy benchmark: • tissue handling • anatomical clearance • laparoscopic tool control • surgeon guided motion Important detail: this is not autonomous surgery. The human surgeon is still in control. But the body holding the tools is starting to change. Would you let a teleoperated humanoid perform surgery on you? #RoboticSurgery #HumanoidRobots #MedicalRobotics #Teleoperation #Surgery

A humanoid robot doing gallbladder surgery sounds fake until you watch the instruments move. This ARCLab / UC San Diego demo shows teleoperated humanoids working through a cholecystectomy benchmark: • tissue handling • anatomical clearance • laparoscopic tool control • surgeon guided motion Important detail: this is not autonomous surgery. The human surgeon is still in control. But the body holding the tools is starting to change. Would you let a teleoperated humanoid perform surgery on you? #RoboticSurgery #HumanoidRobots #MedicalRobotics #Teleoperation #Surgery

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ALERT: Humanoid robots performed the first-ever live surgery on pigs during a preclinical trial at a California university. Engineers and a team of surgeons at the University of California, San Diego successfully used teleoperated humanoid robots for live surgery. In one of the surgeries, the group, along with the humanoid robot, successfully removed a pig's gallbladder. In another case, 2 robots worked side by side to perform the same gallbladder removal. Michael Yip, a faculty member at UC San Diego Jacobs School of Engineering, says, “This can help address the healthcare crisis not only in the United States, but also worldwide.” “This study shows that humanoid robots have a viable future in the field of surgery. You can imagine these robots being deployed in remote communities where staffing is challenging, or in austere environments like search and rescue scenarios where a massive deployment of field medicine is needed in a short period of time.”

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Researchers developed Hitter, a humanoid robot that can play table tennis in real-world settings. It was trained on the Unitree G1 robot to predict the ball’s path, choose the right shot, and react in less than a second. In tests, it achieved 106 consecutive shots with a human and even rallied with another humanoid robot.

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In just one week, Binh and I trained a full-body Unitree G1. Here's a recap: 1. Secured a Unitree G1 humanoid through a LinkedIn post 2. Deployed TWIST2 full-body teleoperation pipelines 3. Adapted TWIST2 for Zed stereo camera & collected full-body teleoperation samples (carried by Binh ) 4. Adapted & fine-tuned NVIDIA Gr00T N1.5 VLA on the TWIST2 public datasets, which I fine-tuned on an 8xNVIDIA H100 Cluster. We picked Gr00T N1.5 as it was trained with Unitree G1 embodiment data. 5. Adapted the TWIST2 codebase to stream in the actions from Gr00T via ZMQ using a co-located NVIDIA H100 for ~200ms inference latency 6. Tested the model in sim, then deployed to the real-world Unitree G1. We streamed a training sample observation to the VLA (as we didn't want to break robot in case real observations were OOD) We were the first team in the world to deploy the full TWIST2 data collection pipeline to the unitree g1 :) Much more work ahead though, which I'll work on as a side-project over the next months: 1. Exploring the various types of 'world models': video backbones, dynamics models, v-jepa-2 models. I believe these will generalize better & train much more data-efficiently than VLM backbones 2. Speeding up inference - I believe low-latency robotics inference will be a big challenge. There are many works in video diffusion which I'd like to test (e.g. SageAttention, SparseAttention, Drifting Models). Perhaps also writing custom CUDA kernels. 3. Economics of inference scaling :) What will be the compute demands as we scale inference up to millions of humanoids? Will it run on edge or on distributed 'co-located' inference clusters? These are questions I'd like to answer. Adapted TWIST2 codebase: Adapted Gr00T-N1.5 codebase: The ETH Robotics Club are doing a cool GTC Golden ticket competition with NVIDIA , so this is my submission :) The DGX Spark compute will get me a long way with initial prototyping & especially working on inference optimization for next-gen Blackwell GPUs #NVIDIAGTC #GOLDENTICKET #ETHRC

In just one week, Binh and I trained a full-body Unitree G1. Here's a recap: 1. Secured a Unitree G1 humanoid through a LinkedIn post 2. Deployed TWIST2 full-body teleoperation pipelines 3. Adapted TWIST2 for Zed stereo camera & collected full-body teleoperation samples (carried by Binh ) 4. Adapted & fine-tuned NVIDIA Gr00T N1.5 VLA on the TWIST2 public datasets, which I fine-tuned on an 8xNVIDIA H100 Cluster. We picked Gr00T N1.5 as it was trained with Unitree G1 embodiment data. 5. Adapted the TWIST2 codebase to stream in the actions from Gr00T via ZMQ using a co-located NVIDIA H100 for ~200ms inference latency 6. Tested the model in sim, then deployed to the real-world Unitree G1. We streamed a training sample observation to the VLA (as we didn't want to break robot in case real observations were OOD) We were the first team in the world to deploy the full TWIST2 data collection pipeline to the unitree g1 :) Much more work ahead though, which I'll work on as a side-project over the next months: 1. Exploring the various types of 'world models': video backbones, dynamics models, v-jepa-2 models. I believe these will generalize better & train much more data-efficiently than VLM backbones 2. Speeding up inference - I believe low-latency robotics inference will be a big challenge. There are many works in video diffusion which I'd like to test (e.g. SageAttention, SparseAttention, Drifting Models). Perhaps also writing custom CUDA kernels. 3. Economics of inference scaling :) What will be the compute demands as we scale inference up to millions of humanoids? Will it run on edge or on distributed 'co-located' inference clusters? These are questions I'd like to answer. Adapted TWIST2 codebase: Adapted Gr00T-N1.5 codebase: The ETH Robotics Club are doing a cool GTC Golden ticket competition with NVIDIA , so this is my submission :) The DGX Spark compute will get me a long way with initial prototyping & especially working on inference optimization for next-gen Blackwell GPUs #NVIDIAGTC #GOLDENTICKET #ETHRC

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A Switzerland-based startup Flexion has created a robotic brain that helps the Unitree G1 move smoothly and work on its own. It uses reinforcement learning, where the robot trains in simulations to learn walking, balancing, and picking objects. In tests, it cleaned a space by finding and placing items in a basket without human help.

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Humanoid and Siemens successfully completed a POC testing humanoid robots in industrial logistics. HMND 01 robot autonomously performed a tote-to-conveyor destacking task during a two-week on-site deployment at the Siemens Electronics Factory in Erlangen. This is the first step in our broader partnership with Siemens as humanoid robots move closer to real-world deployment. Learn more here:

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22,914 次观看 • 7 个月前