Upload GR00T-H N1.7 release checkpoint

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BIAS.md

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+Field                                                                                               |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing:  |  None
+Measures taken to mitigate against unwanted bias:                                                   |  None

EXPLAINABILITY.md

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+# **Explainability**
+
+|Field:|Response:|  
+|:---:|:---:|  
+|Intended Domain:| Open healthcare foundation model for generalized surgical and ultrasound robotics reasoning and skills.|  
+|Model Type: |Robot VLA|  
+|Intended Users:|Researchers and developers working on surgical robotics and ultrasound applications.|  
+|Output:|The model outputs are actions, and the units are floating-point values. This is referred to as "robot action policy." Actions consist of continuous-value vectors that correspond to different motor controls on a robot.|
+|Describe how the model works:|Accepts multimodal inputs such as video, ultrasound, proprioception, and language, then outputs a robot action policy.|  
+|Technical Limitations & Mitigation:| This model is not tested or intended for use in mission critical or clinical applications that require functional safety. Use in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms prior to deployment. More generally, limitations include, but are not limited to:<br>- The model may underperform in operating room environments or device configurations that differ from the training distribution.<br>- Coverage may be limited for rare procedures, uncommon instruments, specialized workflows, or underrepresented institutions.<br>- Performance can vary across sites, sensors, and embodiment interfaces; additional fine-tuning and validation may be required for new deployments.<br><br>Risks and possible mitigations include:<br>Risk: Model underperformance in variable operating room conditions, device configurations, or imaging settings.<br>Mitigation: Expand data coverage across devices/settings and fine-tune for target environments.<br><br>Risk: Integration challenges across surgical/ultrasound platforms with different control interfaces or sensing configurations.<br>Mitigation: Provide embodiment-specific integration guidance and validation procedures.<br><br>Risk: Limited coverage for rare procedures or uncommon instrument/tooling setups.<br>Mitigation: Curate targeted data and evaluate on representative task subsets.|  
+|Verified to have met prescribed quality standards?|Yes|  
+|Performance Metrics:|Success rate, as well as the following:<br>1) if the trajectory is smooth and does not jitter<br>2) if the robot does not hit any other objects<br>3) if the trajectory is natural|  
+|Potential Known Risks:|This model is not tested or intended for clinical applications that require functional safety. The use of the model in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms prior to deployment.|  
+|End User License Agreement:| Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).|

LICENSE

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+NVIDIA License
+1. Definitions
+“Licensor” means any person or entity that distributes its Work.
+“Work” means (a) the original work of authorship made available under this license, 
+which may include software, documentation, or other files, and (b) any additions to or 
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+meaning as provided under U.S. copyright law; provided, however, that for the purposes
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+merely link (or bind by name) to the interfaces of, the Work.
+Works are “made available” under this license by including in or with the Work either (a)
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+the redistribution requirements in Section 3.1) will continue to apply to the Work 
+itself.
+3.3 Use Limitation. The Work and any derivative works thereof only may be used 
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+affiliates’ names, logos, or trademarks, except as necessary to reproduce the 
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PRIVACY.md

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+# **Privacy**
+
+|Field:|Response:|  
+|:---:|:---:|  
+|Generatable or reverse engineerable personal data?|None|   
+|Personal data used to create this model?|No|  
+|How often is dataset reviewed?|Before Release|  
+|Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?|No|
+|Is there provenance for all datasets used in training?|Yes|  
+|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|
+|Is data compliant with data subject requests for data correction or removal, if such a request was made?|Yes|
+|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|

README.md

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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+datasets:
+- nvidia/PhysicalAI-Robotics-Open-H-Embodiment
+base_model:
+- nvidia/GR00T-N1.7-3B
+---
+<div align="center">
+  <a href="https://github.com/NVIDIA-Medtech/GR00T-H">
+    <img src="media/gr00t-h.png" alt="GR00T-H" style="max-width:600px; width:100%; height:auto;">
+  </a>
+  <div align="center">
+  <a href="https://github.com/NVIDIA-Medtech/GR00T-H">
+    <img src="https://img.shields.io/badge/GitHub-grey?logo=GitHub" alt="GitHub Badge">
+  </a>
+  <a href="https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment">
+    <img src="https://img.shields.io/badge/Dataset-Open--H--Embodiment-brightgreen?logo=huggingface" alt="Dataset Badge">
+  </a>
+</div>
+</div>
+
+# GR00T-H-N1.7
+
+# Model Overview
+
+### Description:
+
+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](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)<br>
+You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws. <br>
+
+### 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.
+
+## References(s):
+* Open-H Paper: [Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics](https://arxiv.org/abs/2604.21017)
+* Base Model: [GR00T-N1.7-3B](https://huggingface.co/nvidia/GR00T-N1.7-3B)
+* GR00T Website: [NVIDIA Isaac GR00T](https://developer.nvidia.com/isaac/gr00t)
+* GR00T N1 White Paper: https://arxiv.org/abs/2503.14734
+* Cosmos-Reason2: NVIDIA. "Cosmos-Reason2: An Open, Customizable, Reasoning Vision Language Model." NVIDIA Documentation (2026).<br>
+* 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. "pi0: 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
+
+**This model was developed based on GR00T N1.7.**
+
+**Number of model parameters:** 3B
+
+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](https://github.com/NVIDIA/Isaac-GR00T/blob/main/media/model-architecture.png?raw=true)
+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<br>
+* State: Robot Proprioception<br>
+* Language Instruction: Text<br>
+
+**Input Format(s):**
+* Vision: Variable number of image frames from robot cameras<br>
+* State: Floating Point<br>
+* Language Instruction: String<br>
+
+**Input Parameters:**
+* Vision - Two-Dimensional (2D) - Red, Green, Blue (RGB) image, any resolution<br>
+* State: One-Dimensional (1D) - Floating number vector<br>
+* Language Instruction: One-Dimensional (1D) - String<br>
+
+## Output(s)
+**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, TensorRT
+
+**Supported Hardware Microarchitecture Compatibility:**
+All of the below:
+* NVIDIA Ampere
+* NVIDIA Blackwell
+* NVIDIA Hopper
+* NVIDIA Jetson
+* NVIDIA Lovelace
+
+**Supported Operating System:**
+* Ubuntu
+
+## Model Version(s):
+GR00T-H-N1.7, post-trained from GR00T N1.7
+
+## Training, Testing, and Evaluation Datasets:
+
+### Dataset Overview:
+- **Full Open-H-Embodiment Dataset:** 770 hours; 124,019 episodes; 119 datasets; 20 robot platforms; 50+ institutions <br>
+- **Post-Training Subset:** 601 hours (real-world surgical tasks only); ~63,930 episodes; 58 datasets; 7 robot platforms <br>
+- **Dataset partition:** Training 98%, Testing N/A (real-world robot evaluation only), Validation 2% <br> 
+
+### Training Data Summary
+
+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.
+
+For more information, see the [Open-H-Embodiment project page](https://open-h.github.io/open-h-embodiment/).
+
+## Training Dataset:
+
+- **Data Modality:** Video, Kinematics
+- **Video Training Data Size:** 601 hours
+- **Kinematic Training Data Size:** 601 hours
+- **Data Collection Method:** Hybrid: Automatic/Sensors, Human, Synthetic
+- **Labeling Method:** Hybrid: Automatic/Sensors, Human, Synthetic
+- **Properties:** 
+  - 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
+- **Labeling Method:** Hybrid: Automatic/Sensors, Human, Synthetic
+- **Properties:**
+  - 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. <br> 
+
+Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. <br>
+
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](EXPLAINABILITY.md), [Bias](BIAS.md), [Safety & Security](SAFETY_and_SECURITY.md), and [Privacy](PRIVACY.md) Subcards.
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

SAFETY_and_SECURITY.md

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+# **Safety & Security**
+
+|Field:|Response:|  
+|:---:|:---:|  
+|Model Application(s):|Robotic surgical and ultrasound research and development.|  
+|Describe life-critical application (if present):|This model is intended for research and is not tested or intended for clinical or mission critical applications that require functional safety. It is not for deployment in clinical settings or for medical decision-making. Any real-world use requires independent safety review and regulatory clearance.|
+|Use Case Restrictions:| Abide by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)|  
+|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.|

config.json

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+{
+  "action_horizon": 50,
+  "add_pos_embed": true,
+  "apply_sincos_state_encoding": false,
+  "architectures": [
+    "Gr00tN1d7"
+  ],
+  "attn_dropout": 0.2,
+  "attn_implementation": null,
+  "backbone_embedding_dim": 2048,
+  "backbone_trainable_params_fp32": true,
+  "color_jitter_params": {
+    "brightness": 0.3,
+    "contrast": 0.4,
+    "hue": 0.08,
+    "saturation": 0.5
+  },
+  "crop_fraction": 0.95,
+  "diffusion_model_cfg": {
+    "attention_head_dim": 48,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "interleave_self_attention": true,
+    "norm_type": "ada_norm",
+    "num_attention_heads": 32,
+    "num_layers": 32,
+    "output_dim": 1024,
+    "positional_embeddings": null
+  },
+  "dtype": "bfloat16",
+  "exclude_state": false,
+  "formalize_language": true,
+  "hidden_size": 1024,
+  "image_crop_size": [
+    230,
+    230
+  ],
+  "image_target_size": [
+    256,
+    256
+  ],
+  "letter_box_transform": false,
+  "load_bf16": true,
+  "max_action_dim": 132,
+  "max_num_embodiments": 32,
+  "max_seq_len": 1024,
+  "max_state_dim": 132,
+  "model_dtype": "bfloat16",
+  "model_name": "nvidia/Cosmos-Reason2-2B",
+  "model_type": "Gr00tN1d7",
+  "noise_beta_alpha": 1.5,
+  "noise_beta_beta": 1.0,
+  "noise_s": 0.999,
+  "num_inference_timesteps": 4,
+  "num_timestep_buckets": 1000,
+  "random_history_crop": true,
+  "random_rotation_angle": 0,
+  "reproject_vision": false,
+  "rtc_ramp_rate": 6.0,
+  "select_layer": 16,
+  "shortest_image_edge": 256,
+  "soft_prompt_lr_scale": 1.0,
+  "soft_prompt_num_tokens": 32,
+  "state_dropout_prob": 0.0,
+  "state_dropout_prob_per_embodiment": {
+    "cmr_versius": 1.0,
+    "jhu_imerse_dvrk": 1.0,
+    "obuda_dvrk": 1.0,
+    "stanford_dvrk_real": 1.0,
+    "ucb_dvrk": 1.0,
+    "ucsd_dvrk": 1.0,
+    "hamlyn_dvrk_30hz": 1.0,
+    "jhu_imerse_dvrk_mono": 1.0,
+    "jhu_imerse_star_il": 1.0,
+    "jhu_lscr_dvrk_smarts": 1.0,
+    "rob_surgical_bitrack": 1.0,
+    "tud_tundra_ur5e": 1.0,
+    "turin_mitic_ex_vivo": 1.0,
+    "ustc_torin_tuodao": 1.0
+  },
+  "state_gaussian_noise_std": 0.0,
+  "state_history_length": 1,
+  "transformers_version": "4.57.6",
+  "tune_diffusion_model": true,
+  "tune_linear": true,
+  "tune_llm": true,
+  "tune_projector": true,
+  "tune_top_llm_layers": 0,
+  "tune_visual": true,
+  "tune_vlln": true,
+  "use_albumentations": true,
+  "use_alternate_vl_dit": true,
+  "use_flash_attention": true,
+  "use_future_tokens": false,
+  "use_mean_std": false,
+  "use_percentiles": true,
+  "use_soft_prompts": false,
+  "use_vl_self_attention": false,
+  "use_vlln": true,
+  "vl_self_attention_cfg": null
+}

embodiment_id.json

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+{
+  "oxe_droid_relative_eef_relative_joint": 24,
+  "simpler_env_google": 0,
+  "simpler_env_widowx": 1,
+  "libero_sim": 2,
+  "new_embodiment": 10,
+  "jhu_imerse_dvrk": 3,
+  "cmr_versius": 4,
+  "ucb_dvrk": 5,
+  "sanoscience_sim": 6,
+  "tum_sonata_franka": 7,
+  "hamlyn_dvrk_15hz": 9,
+  "hamlyn_dvrk_30hz": 11,
+  "ustc_torin_tuodao": 12,
+  "ucsd_dvrk": 14,
+  "jhu_imerse_dvrk_mono": 15,
+  "rob_surgical_bitrack": 16,
+  "stanford_dvrk_real": 17,
+  "obuda_dvrk": 18,
+  "polyu_sim": 19,
+  "moon_maestro": 21,
+  "jhu_lscr_dvrk_miracle": 22,
+  "jhu_lscr_dvrk_smarts": 23,
+  "tud_tundra_ur5e": 25,
+  "turin_mitic_ex_vivo": 26,
+  "jhu_imerse_star_il": 27
+}

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