gr00t_1.5

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GR00T-N1.5-3B

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Description:

NVIDIA Isaac GR00T N1.5 is an open foundation model for generalized humanoid robot reasoning and skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments. Developers and researchers can post-train GR00T N1.5 with real or synthetic data for their specific humanoid robot or task.<br><br> Isaac GR00T N1.5-3B is the medium-sized version of our model built using pre-trained vision and language encoders, and uses a flow matching action transformer to model a chunk of actions conditioned on vision, language and proprioception.<br><br> This model is ready for non-commercial use.

License/Terms of Use

Nvidia License<br> You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws. <br>

Deployment Geography:

Global

Use Case:

Researchers, Academics, Open-Source Community: AI-driven robotics research and algorithm development. Developers: Integrate and customize AI for various robotic applications. Startups & Companies: Accelerate robotics development and reduce training costs.

Reference(s):

NVIDIA-EAGLE: Li, Zhiqi, et al. "Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models." arXiv preprint arXiv:2501.14818 (2025).<br> Rectified Flow: Liu, Xingchao, and Chengyue Gong. "Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow." The Eleventh International Conference on Learning Representations”.<br> Flow Matching Policy: Black, Kevin, et al. "π0: A Vision-Language-Action Flow Model for General Robot Control." arXiv preprint arXiv:2410.24164 (2024).<br>

Model Architecture:

Architecture Type: Vision Transformer, Multilayer Perceptron, Flow matching Transformer

Isaac GR00T N1.5 uses vision and text transformers to encode the robot's image observations and text instructions. The architecture handles a varying number of views per embodiment by concatenating image token embeddings from all frames into a sequence, followed by language token embeddings.

To model proprioception and a sequence of actions conditioned on observations, Isaac GR00T N1.5-3B uses a flow matching transformer. The flow matching transformer interleaves self-attention over proprioception and actions with cross-attention to the vision and language embeddings. During training, the input actions are corrupted by randomly interpolating between the clean action vector and a gaussian noise vector. At inference time, the policy first samples a gaussian noise vector and iteratively reconstructs a continuous-value action using its velocity prediction.

In GR00T-N1.5, the MLP connector between the vision-language features and the diffusion-transformer (DiT) has been modified for improved performance on our sim benchmarks. Also, it was trained jointly with flow matching and world-modeling objectives.

Network Architecture: The schematic diagram is shown in the illustration above. Red, Green, Blue (RGB) camera frames are processed through a pre-trained vision transformer (SigLip2). Text is encoded by a pre-trained transformer (T5) Robot proprioception is encoded using a multi-layer perceptron (MLP) indexed by the embodiment ID. To handle variable-dimension proprio, inputs are padded to a configurable max length before feeding into the MLP. Actions are encoded and velocity predictions decoded by an MLP, one per unique embodiment. The flow matching transformer is implemented as a diffusion transformer (DiT), in which the diffusion step conditioning is implemented using adaptive layernorm (AdaLN).

Input:

Input Type:

• Vision: Image Frames<br>
• State: Robot Proprioception<br>
• Language Instruction: Text<br>

Input Format:

• Vision: Variable number of 224x224 uint8 image frames, coming from robot cameras<br>
• State: Floating Point<br>
• Language Instruction: String<br>

Input Parameters:

• Vision: 2D - RGB image, square<br>
• State: 1D - Floating number vector<br>
• Language Instruction: 1D - String<br>

Output:

Output Type(s): Actions<br> Output Format Continuous-value vectors<br> Output Parameters: [Two-Dimensional (2D)] <br> Other Properties Related to Output: Continuous-value vectors correspond to different motor controls on a robot, which depends on Degrees of Freedom of the robot embodiment.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>

Software Integration:

Runtime Engine(s): PyTorch

Supported Hardware Microarchitecture Compatibility: All of the below:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Jetson
• NVIDIA Hopper
• NVIDIA Lovelace

[Preferred/Supported] Operating System(s):

• Linux

Model Version(s):

Version 1.5.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security), and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Resources

• Previous Version: https://huggingface.co/nvidia/GR00T-N1-2B<br>
• Blogpost: https://nvidianews.nvidia.com/news/foundation-model-isaac-robotics-platform
• Community Article with the tutorial how to finetune on SO100/101: https://huggingface.co/blog/nvidia/gr00t-n1-5-so101-tuning