NVIDIA introduced GR00T 2 at the Conference on Robot Learning (CoRL) in Austin on November 9, calling it an open frontier world action model built on its Cosmos world models. The 2-billion-parameter model predicts future video and robot actions together, and was trained on diverse robot and human data, including about 402,730 hours of first-person human video.

NVIDIA reports GR00T 2 completed 40 of 50 evaluated tasks at least once with no task-specific demonstrations, reached 71% average success with one demonstration per task, and 95% with five. One pretrained checkpoint covers parallel-gripper arms, dexterous hands and a humanoid, and NVIDIA is releasing 100,000 hours of the human egocentric data openly under a CC BY 4.0 licence.

Alongside the model, NVIDIA shipped Isaac Lab 3.0, its open-source robot-learning framework, which lets agents set up training environments and apply a new reinforcement post-training library to foundation models like GR00T. The GR00T 2 code is listed as arriving in December, and no licence or release date for the model weights has been announced yet — Foxconn, Siemens and Nexuni are testing the model, not deploying it.