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A generalist model, not a dedicated robot policy, pulled off physical control by reasoning and using tools. Here's the four-step approach Gemini 4 figured out, given high-level instructions and the overall task: 1. Watched a human throw, and mapped the motion to its own joints. 2. Used its cameras...

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The Humanoid Hub

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10,160 görüntüleme • 25 gün önce •via X (Twitter)

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Rackham Rishel profil fotoğrafı
Rackham Rishel25 gün önce

Watching a person move and mapping that onto its own joints without a dedicated robot policy is quietly enormous

Brian Bellia profil fotoğrafı
Brian Bellia25 gün önce

One thing Apptronik have definitely got right with their humanoid, in my opinion, is the head, which I believe is very important. I love the two camera eyes and the soft flashing LED mouth when it speaks. 👍👍👍

True North Take profil fotoğrafı
True North Take25 gün önce

A general model doing physical control via tool use is a big shift from bespoke policies. If that holds, the value moves to hardware makers like Apptronik and component suppliers. Curious how latency looks in real time.

Fabien Musty profil fotoğrafı
Fabien Musty25 gün önce

Generalist models are at least the glue that will make humanoids coherent in all situations. They need a relatable and flexible default behavior in-between learned skills.

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Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught Apptronik's Apollo humanoid to sink 7 throws in a row.

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A robot stole the spotlight with its killer dance moves during an Erling Haaland lookalike contest in Miami. Despite its best efforts, the robot Haaland did not win, and the title went to a human.
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Additionally, looking towards the future, we’re releasing PARTNR: a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. Built on Habitat 3.0, it’s the largest benchmark of its kind to study and evaluate human-robot collaboration in household activities By providing a standardized benchmark and dataset we hope to enable new research on robots that can not only operate in isolation, but in collaboration with people. Details and code ➡️
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Additionally, looking towards the future, we’re releasing PARTNR: a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. Built on Habitat 3.0, it’s the largest benchmark of its kind to study and evaluate human-robot collaboration in household activities By providing a standardized benchmark and dataset we hope to enable new research on robots that can not only operate in isolation, but in collaboration with people. Details and code ➡️

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The future of sports is evolving in China. A robot just played tennis against a human at the World Humanoid Robot Games in Beijing with zero human control, tracking the ball and returning shots entirely on its own. How long before robots have their own Olympics?

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An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

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Want to see Gemini 2.0 Flash Thinking in action? Check out this demo where the model solves a physics problem and explains its reasoning.
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Want to see Gemini 2.0 Flash Thinking in action? Check out this demo where the model solves a physics problem and explains its reasoning.

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The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.

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RoboParty built a humanoid robot in 4 months. Now, the full project is open source. ROBOTO ORIGIN stands about 1.25 m tall, weighs 34 kg and has 23 degrees of freedom. The technical details: • 120 Nm peak torque at the leg joints • 48 V, 15 Ah battery • Mechanical CAD, PCB designs and BOM • Firmware, motor control and ROS 2 deployment • URDF/MJCF models for simulation • Reinforcement learning with Isaac Lab and RSL-RL • Sim-to-sim transfer to MuJoCo and sim-to-real validation The robot can walk, run and jump. What caught my attention is the training to hardware workflow. You can study how learned motion policies move from simulation to a physical humanoid.

Techniahqrobot | humanoid robots

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An LLM robot agent’s success rate goes up as it practices. Did it learn anything, or did it just try more often? And do the skills and memories it saves still help on new tasks, in a new simulator, on a real robot? We built SEAR to answer both. To our knowledge, it’s the first large scale controlled study of self-evolving robot agents. The paper is now on arXiv 📄 The agent improves a robot in one of three ways: it writes code that becomes the policy, it acts live in the control loop, or it trains its own VLA. 7 frontier LLMs, 330 sim tasks, 5 real-robot tasks, 4+ years of agent runtime, with a matched control for every comparison and a sealed judge on unseen scenes. A few findings surprised us 🧵 1/7

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I built this little #robot I ordered from Hugging Face Pollen Robotics and connected it to the Google Gemini AI model through its API gateway. The robot wanted its own personality - so I gave it an #ATX (Texas) vibe and you can see the result in the video. Really awesome - it even answers questions and carries on a conversion. The vision and hearing are great - it can identify and follow what it sees (with a reasonable delay). My next goal is to get it to work with a 100% local AI model. Specs: 2 cameras, 4 microphones, a speaker, and a wifi interface to a Raspberry Pi built-in controller. I plan to demo it at the InnoTech #InnoTechAUS event on May 12th. Texas McCombs Austin Tech Council TechRanch

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