GR00T-H-N1.7 is a post-trained variant of NVIDIA Isaac GR00T N1.7 for surgical robots. It builds on the GR00T N1.7 VLA foundation and adapts it using the Open-H embodiment dataset.
This model is ready for commercial use.
The neural network architecture is inherited from the GR00T N1.7 series of models, combining a vision-language foundation model with a diffusion transformer head that denoises continuous actions.
License/Terms of Use:
NVIDIA Open Model License
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Deployment Geography:
Global
Use Case:
Researchers, Academics, Open-Source Community: Healthcare-focused robotics research and algorithm development.
Intended Use
GR00T-H-N1.7 is intended for use in robotics R&D, including exploration of surgical robotics and robotic ultrasound policies, benchmarking, and method development. It is not intended for clinical deployment, patient care, or medical decision-making.
Cosmos-Reason2: NVIDIA. "Cosmos-Reason2: An Open, Customizable, Reasoning Vision Language Model." NVIDIA Documentation (2026).
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.
Flow Matching Policy:
Black, Kevin, et al. "pi0: A Vision-Language-Action Flow Model for General Robot Control." arXiv preprint arXiv:2410.24164 (2024).
GR00T-H-N1.7 uses Cosmos-Reason2-2B 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, GR00T-H-N1.7 uses a flow matching transformer. The flow matching transformer interleaves self-attention over proprioception and actions with cross-attention to the Cosmos-Reason2-2B 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.
Network Architecture:
image/png
The schematic diagram is shown in the illustration above.
Red, Green, Blue (RGB) camera frames are processed through a pre-trained vision transformer (SigLip2).
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(s):
Input Type(s):
Vision: Image Frames
State: Robot Proprioception
Language Instruction: Text
Input Format(s):
Vision: Variable number of image frames from robot cameras
State: Floating Point
Language Instruction: String
Input Parameters:
Vision - Two-Dimensional (2D) - Red, Green, Blue (RGB) image, any resolution
State: One-Dimensional (1D) - Floating number vector
Language Instruction: One-Dimensional (1D) - String
Output(s)
Output Type(s): Actions Output Format Continuous-value vectors Output Parameters: Two-Dimensional (2D) 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.
Software Integration:
Runtime Engine(s): PyTorch, TensorRT
Supported Hardware Microarchitecture Compatibility:
All of the below:
GR00T-H-N1.7 is adapted from the upstream GR00T N1.7 foundation model using an Open-H post-training phase. The full Open-H-Embodiment dataset contains 770 hours of paired video and kinematic data across 124,019 episodes with synchronized streams such as video, kinematics, force/torque, ultrasound, and domain-specific sensors. For post-training, a 601-hour real-world surgical subset of the full 770-hour corpus is used. Only real-world surgical datasets are used; ultrasound, endoscopy, and simulation data is left for future work. The Versius-500 contribution is capped at 20% of training steps to prevent any single embodiment from dominating the loss signal; remaining datasets are sampled proportionally to their size.
GR00T-H-N1.7 was trained on 7 robot platforms across 58 datasets: CMR Versius, dVRK, dVRK-Si, Rob Surgical BiTrack, KUKA LBR iiwa, USTC Torin, and UR5e.
To enable better cross-embodiment transfer, the action space was standardized to relative end-effector (EEF) positioning. Additionally, camera configurations were standardized to include only (A) a single third-person monocular view, or (B) a third-person monocular view with wrist camera(s) and/or additional modalities (e.g., ultrasound images).
The Open-H dataset was collected by more than 50 institutions across the globe. Data collection took place in various settings, including simulation, benchtop, ex vivo, in vivo, and clinical environments. Depending on the dataset, robots were teleoperated either programmatically or by engineers, researchers, medical students, or professional surgeons.
Open-H is a healthcare robotics dataset comprised of time-synchronized video and kinematics, as well as text labels describing the task being completed.
Evaluation Dataset:
Data Collection Method: Hybrid: Automatic/Sensors, Human, Synthetic
2% of the training dataset was held-out for training-time validation.
Primary evaluations are conducted in the real-world without a dataset.
Inference:
Acceleration Engine: TensorRT
Test Hardware: NVIDIA Ampere, Ada, and Blackwell GPUs
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. 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.
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