# Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T

[NVIDIA Developer Blog](https://yomu.fyi/company/nvidia-developer-blog) · Elizabeth Goodman · Jul 7, 2026

**Type:** Announcement

## Summary

Humanoid robotics development suffers from fragmented tooling, siloed software ecosystems, and incompatible data formats that hinder reproducible workflows. NVIDIA introduced the Isaac GR00T Development Platform, an open-source, modular humanoid development environment that connects simulation setup, demonstration capture, policy training, evaluation, and hardware deployment. Central to this platform is the Isaac GR00T 1.7 vision-language-action model, which features a 3-billion-parameter base checkpoint licensed under Apache 2.0. The architecture adopts a Cosmos-Reason2-2B backbone and supports full pipeline export to ONNX and TensorRT for deployment on Jetson Thor. In a reference pick-and-place manipulation task, developers capture teleoperated demonstrations with Isaac Teleop, convert trajectories into LeRobot format, post-train GR00T 1.7, and evaluate the closed-loop policy inside Isaac Lab-Arena.

## Context

Humanoid robotics development is slowed by fragmented software pipelines, incompatible data formats, and manual tool integrations that force developers to spend extensive time configuring infrastructure instead of building robot capabilities.

## Approach / What changed

NVIDIA created the open-source Isaac GR00T Development Platform, unifying simulation in Isaac Lab-Arena, demonstration capture with Isaac Teleop, post-training on the 3-billion-parameter GR00T 1.7 vision-language-action model, closed-loop evaluation, and deployment via Isaac ROS and Jetson Thor.

## Takeaways

- The open-source Isaac GR00T 1.7 model is licensed under Apache 2.0, has a 3-billion-parameter base checkpoint, and is pretrained on approximately 32K hours of real demonstration data alongside 8K hours of simulated rollouts.
- GR00T 1.7 replaces the prior Eagle vision backbone with Cosmos-Reason2-2B (Qwen3-VL architecture) to support flexible resolution and encode images in their native aspect ratio without padding.
- During teleoperation setup, chosen controllers such as AGILE WBC and PinkIK determine the joint-space targets that form the policy's underlying training distribution.

**Tags:** [Deployment](https://yomu.fyi/topic/deployment), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Open Source](https://yomu.fyi/topic/open-source)

[Read original post](https://developer.nvidia.com/blog/develop-humanoid-robot-policies-end-to-end-with-nvidia-isaac-gr00t)