AI Agents
IsaacLab
Isaac Lab — NVIDIA's unified framework for robot learning, with 30+ environments and multi-physics simulation.
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View guide →NVIDIA Isaac GR00T N1.7 — an open foundation model for generalist robots, ready to fine-tune and deploy.
Isaac-GR00T is an open-source ai agents project by NVIDIA (NVIDIA/Isaac-GR00T on GitHub). It has 8,173 GitHub stars and 1,486 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 7 October 2026.
Check the hardware requirements and your robot/dataset format (LeRobot-style datasets are used in the workflow), and install git-lfs before cloning since demo data uses it.
Clone NVIDIA/Isaac-GR00T from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Clone with submodules, install with uv (or the pip alternative), and add the system GL libraries the README lists.
Run a documented inference or evaluation example with the demo data, then fine-tune on a small slice of your own dataset.
A pretrained vision-language-action model intended as a base for many robots and tasks, not a single-task policy.
N1.7 replaces the older Eagle backbone with Cosmos-Reason2-2B (Qwen3-VL), encoding images at native aspect ratio without padding.
State and action dimensions expand to 132 with an action horizon of 40, and the flow-matching DiT action head is updated for N1.7.
Fine-tune on multiple dataset paths at once with mixture weighting (ds_weights_alpha) for easier multi-source training.
Full-pipeline ONNX and TensorRT export, with hardened policy serving, rollout recording, evaluation and config validation.
Documented results across RoboCasa (including GR1 tabletop tasks), SimplerEnv and real G1 robot evaluation.
Get the source from the official GitHub repository for Isaac-GR00T.
git clone https://github.com/NVIDIA/Isaac-GR00T.git
cd Isaac-GR00TInstall git-lfs first (required for the demo data), then clone the repository with its submodules as the README directs.
sudo apt install git-lfs && git lfs install
git clone --recurse-submodules https://github.com/NVIDIA/Isaac-GR00TInstall in editable mode with uv (the README's primary path) after cloning; a pip alternative is documented there too.
uv pip install -e . --verbose # pip alternative: pip install -e . — see READMENVIDIA, through the NVIDIA/Isaac-GR00T repository, with the product page at developer.nvidia.com/isaac/gr00t.
Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
The model and code are free under Apache-2.0 with no API key, but you need capable GPU hardware and your own robot demonstration data for meaningful fine-tuning.
Training from scratch needs far more data and compute for basic competence. GR00T starts from a pretrained generalist model you fine-tune — you still need quality demonstrations and GPUs, but the starting point already understands scenes and language.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — run a documented inference or evaluation example with the demo data, then fine-tune on a small slice of your own dataset.
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