MuJoCo

Headlines · 6

1. 2d
   Metatron v1 brings unified Unitree G1 humanoid control policy

   A report describes Metatron v1 as a single learned policy for the 29-joint Unitree G1 humanoid, handling walking, scene interaction and fall recovery. The MaskedMimic-style transformer student was released under the NVIDIA Open Model License and integrated with ProtoMotions. The release contains three non-interchangeable packages: a native PyTorch checkpoint of about 459 MB for simulator and supervised real-robot workflows, a single-robot browser ONNX pipeline, and a six-robot batched ONNX network requiring external observation processing. All three use epoch-6410 weights, while the ONNX exports run on MuJoCo WASM and ONNX Runtime Web through WebGPU or WASM.

   r/LocalLLaMA · 🔥 2
2. 3d
   Google and NASA Test AI Robot for Deep-Space Support

   Google and NASA are developing an AI-enabled robot that could assist astronauts with cargo handling, equipment organization, logistics, and medical-support tasks during deep-space missions. The physical proof-of-concept has completed initial laboratory testing, while Google says fine-tuned models can run locally on small, low-power edge-computing devices. In early simulation tests using DeepMind's MuJoCo, the robot achieved an 85% success rate on tasks including medical-bag localization and transport, according to the vendor claim reported by 科技新报.

   科技新报 · 🔥 0
3. 6d
   Open Duck Mini v2 advances open-source sim-to-real robotics

   The apirrone/Open_Duck_Mini repository says Open Duck Mini v2 is a roughly 42-centimeter-tall miniature version of Disney’s BDX Droid. The project uses reinforcement learning to train walking policies and documents progress on transferring them from simulation to the physical robot, with CAD, build, runtime and policy-training resources available. The project estimates a full bill of materials below $400, but that figure has not been independently verified.

   GitHub Trending · Python · 🔥 0
4. 6d
   Pollen Robotics presents the Microduck biped robot

   Pollen Robotics’ Microduck repository describes a roughly 25 cm, 800 g biped duck robot whose locomotion uses reinforcement-learning policies. The vendor says its Rockchip RK3566 runs a 50 Hz control loop for 15 servos. Policies are trained in the companion microduck_rl project using MuJoCo, PPO, domain randomization and a sim-to-real process, then exported to ONNX. The repository includes robot code, documentation and a simulator, and provides a purchase link.

   GitHub Trending（每日） · 🔥 0
5. Oct 21
   Agnes AI demos self-evolving agents: Honor of Kings team and test-time-training robot arm

   At the 2026 Physical AI and World Models conference in Nanjing, Agnes AI (爱思科技) livestreamed a 5v5 Honor of Kings match in which an AI coach rewrote the team's tactical playbook after each game, lifting its win rate against built-in bots from 0% to 100% over five revisions, according to the company's booth demo reported by 新智元. No model parameters were trained in that experiment; the coach, described as the forthcoming Agnes 3.0 Pro, only edited prompts, action choices and code, with each version required to pass researcher-set tests before play. A separate demo showed a Franka Panda arm in MuJoCo simulation updating its own weights during work via Agnes Embed TTT, raising task success from roughly 30% to about 90% while cutting iteration time to as little as 30 minutes to an hour. Agnes frames both routes as part of its open-source Agnes Harness agent infrastructure and its "万物共脑" (one brain for everything) pitch, and says a version of the harness also coordinated simulated Mars-base devices and booth robots.

   新智元 · 🔥 0
6. Oct 16
   Google's Gemini 4 Argon targets the full robotics pipeline

   An AI Frontline article republished by 36Kr says Google's Gemini 4 (Argon), described in Google's developer blog as a general physical-reasoning model for interacting with the physical world, scores 87% on the ERQA embodied-reasoning benchmark, ahead of Claude Opus 5.5 (82.3%) and GPT-6 Astra (81%). The piece says Argon can control real robots without robot-specific fine-tuning, generate runnable MuJoCo simulation code from images or video (Real2Sim), and automatically label and clean teleoperation data, with a context window of up to 1M tokens. It also cites community experiments on the Antigravity platform and Hacker News, and claims that fine-tuning a VLA model on 4,500-plus Argon-generated virtual trajectories raised real-robot success from 56% to 75% and cut average task time from 35 to 24 seconds. All figures come from Google or the article's own account and have not been independently reproduced.

   36氪 人工智能 · 🔥 0

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