NVIDIA Unveils Robot Model GR00T 2 in Austin, Texas: 40 Percent Average Zero-Shot Success on 50 New Tasks, Rising to 95 Percent With Five Demonstrations Per Task
Austin, Texas — NVIDIA introduced GR00T 2 on November 9 at the Conference on Robot Learning, or CoRL. NVIDIA described it as an open frontier world action model, announced alongside further Isaac Lab 3.0 work and two new open evaluation benchmarks.
According to NVIDIA, GR00T 2 is built on its Cosmos world models and uses a 2-billion-parameter transformer that predicts future video and robot actions together in one diffusion transformer. NVIDIA's research page lists code and weights as available in December.
NVIDIA reported that GR00T 2 achieved 40 percent average success with no demonstrations on 50 tasks absent from the target robot's training data. With one demonstration per task, average success rose to 71 percent, and with five demonstrations per task it rose to 95 percent.
For training data, NVIDIA said pretraining drew on large volumes of first-person human video and robot teleoperation data, including a 100,000-hour open-source egocentric dataset. The model can be driven through an application programming interface and supporting tools to run skills in sequence and recover from errors.
Isaac Lab 3.0, presented at the same event, lets researchers switch between the PhysX and Newton physics solvers and apply a new reinforcement-learning post-training library. NVIDIA said that in a vision-based dexterous manipulation benchmark, Isaac Lab 3.0 with Newton reached up to 2.5 times the training throughput of Isaac Lab 2.3 with PhysX.
NVIDIA also released the developer preview of Isaac Lab-Arena 0.4 and two open benchmarks, DexBench and VABAR, designed to measure robot dexterity and adaptability on industrial tasks. DexBench, developed by RLWRLD with NVIDIA, covers 15 tasks across four scenes, while VABAR, from UC Berkeley and NVIDIA, covers six industrial tasks.
On early use, NVIDIA said Nexuni is testing GR00T 2 for laundry handling, Foxconn is applying it to server-manufacturing automation and Siemens is evaluating industrial use cases. MicroAGI used a reinforcement-learning post-trained GR00T 2 to swap Ethernet cables with a robotic arm.
Sources: The Robot Report on NVIDIA's November 9 announcements: https://www.therobotreport.com/nvidia-launches-gr00t-2-isaac-lab-3-0-corl/ ; NVIDIA Developer Forums announcement summary, November 9, 2026: https://forums.developer.nvidia.com/t/news-nvidia-at-corl-2026-advancing-robot-learning/388596
Published by GSM · USA (original Chinese edition)
Photo: Wikipedia/Wikimedia Commons 词条主图：Nvidia
