Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T | NVIDIA Technical Blog

This post introduces the NVIDIA Isaac GR00T Development Platform as an end-to-end solution for humanoid robot policy development. It details the GR00T 1.7 vision-language-action (VLA) model, which uses a Cosmos-Reason2-2B backbone and is pretrained on ~32K hours of real human demonstration and ~8K hours of simulation data. Key advancements include expanded deployment support via ONNX/TensorRT export, enhanced long-horizon task reasoning through task/subtask decomposition, and improved benchmarks on DROID and SimplerEnv. The post outlines a simulation workflow for a pick-and-place task, covering environment setup, data collection with Isaac Teleop, data conversion to LeRobot format, post-training GR00T 1.7, and policy evaluation in Isaac Lab Arena, emphasizing the use of AGILE WBC for static tasks and the role of joint-space targets as training signals.