---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
language:
  - en
tags:
  - robotics
  - vision-language-action
  - vla
  - surgical-robotics
  - ultrasound
  - robot-learning
  - gr00t
  - nvidia
  - fine-tuned
base_model: nvidia/GR00T-H-N1.7
datasets:
  - nvidia/PhysicalAI-Robotics-Open-H-Embodiment
library_name: gr00t
pipeline_tag: robotics
---

# GR00T-H-N1.7 — TUM SonATA Franka Fine-Tune

Fine-tuned checkpoint of [nvidia/GR00T-H-N1.7](https://huggingface.co/nvidia/GR00T-H-N1.7)
on the TUM SonATA robotic ultrasound subset of the
[Open-H Embodiment dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment).

The model controls a Franka Panda robot performing ultrasound probe manipulation tasks
(placement, transverse scanning, anatomical navigation) on abdominal, thyroid, and arm phantoms.

## Model Details

| Property | Value |
|----------|-------|
| **Base model** | nvidia/GR00T-H-N1.7 |
| **Embodiment** | `TUM_SONATA_FRANKA` |
| **Robot** | Franka Panda + ultrasound probe |
| **Task** | Robotic sonography — probe placement, scanning, navigation |
| **Action space** | 9D REL_XYZ_ROT6D (relative EEF pose, 50-step horizon @ 30Hz) |
| **State inputs** | 7D joint angles + 6D force/torque |
| **Camera inputs** | Third-person view · Wrist camera · Ultrasound image |
| **Language** | Natural language instructions per episode |

## Training Details

| Setting | Value |
|---------|-------|
| **Dataset** | [Open-H Embodiment — TUM SonATA](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment/tree/main/Ultrasound/tum/computer_aided_medical_procedures_camp_lab/sonata_all_update/sonata_all) |
| **Episodes** | 2,397 total · 1,677 used for training |
| **Frames** | 633,604 total @ 30 Hz |
| **Hardware** | 6 × NVIDIA RTX A6000 (49 GB) |
| **Training steps** | 20,000 |
| **Global batch size** | 192 (32 per GPU) |
| **Learning rate** | 8e-4 peak, cosine decay, 5% warmup |
| **Optimizer** | AdamW (weight decay 1e-5) |
| **Tuned components** | Projector + diffusion action head (backbone frozen) |
| **Framework** | DeepSpeed ZeRO-2, PyTorch 2.7 |
| **Final loss** | ~0.026 at step 20,000 |
| **Training time** | ~28 hours |

### Training Notes

A gradient spike (loss ≈ 55, grad norm ≈ 155) occurred at approximately step 2,500 when
the learning rate reached its peak. Training recovered automatically via gradient clipping.
For future runs at this batch size, a peak learning rate of **4e-4** or lower is recommended.

## Usage

```python
from gr00t.model.policy import Gr00tPolicy

policy = Gr00tPolicy(
    model_path="hemanth21k/GR00T-H-N1.7-TUM-SonATA-Franka",
    embodiment_tag="TUM_SONATA_FRANKA",
    denoising_steps=4,
)
```

See the [GR00T-H getting started guide](https://github.com/NVIDIA-Medtech/GR00T-H/tree/main/getting_started)
for full inference setup, including dataset format and processor configuration.

## Dataset

Training data comes from the
[NVIDIA PhysicalAI Open-H Embodiment dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment),
specifically the TUM SonATA subset:

```
Ultrasound/tum/computer_aided_medical_procedures_camp_lab/sonata_all_update/sonata_all
```

The SonATA dataset is a robotic sonography collection from TUM's Computer Aided Medical
Procedures (CAMP) Lab, containing synchronized ultrasound imaging, external RGB cameras,
contact force/torque measurements, robot joint state, and natural language instructions
collected from abdominal, thyroid, and arm phantoms.

| Subset | Episodes | Tasks |
|--------|----------|-------|
| SonATA_abdomen | 1,533 | 287 |
| SonATA_arm | ~1,107 | — |
| SonATA_thyroid | ~780 | — |

## Acknowledgements

This work was conducted at the **Quantitative Bio Imaging Lab (QBIL)**
at **The University of Texas at Dallas**.

Research reported in this publication was supported in part by the National Cancer Institute
of the National Institutes of Health under Award Numbers **R01CA288379** and **R01CA204254**,
and by the Cancer Prevention and Research Institute of Texas (CPRIT) under Award Number
**RP240289**. The content is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institutes of Health.

Computing resources were provided by the QBIL Lab GPU cluster at UT Dallas.

- GitHub: [Hemanth21k/world-models](https://github.com/Hemanth21k/world-models)
- Contact: [satyasaihemanth.p@utdallas.edu](mailto:satyasaihemanth.p@utdallas.edu)

## License

The fine-tuned weights inherit the
[NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
from the base GR00T-H-N1.7 model. The training code is released under Apache-2.0 via
[Hemanth21k/world-models](https://github.com/Hemanth21k/world-models).

## Citation

If you use this model, please cite:

```bibtex
@software{pasupuleti2026worldmodels,
  author    = {Pasupuleti, Hemanth},
  title     = {world-models: Unified interface for testing and extending
               world model architectures for Physical AI},
  year      = {2026},
  url       = {https://github.com/Hemanth21k/world-models},
  note      = {Quantitative Bio Imaging Lab (QBIL), The University of Texas at Dallas.
               Supported by NIH R01CA288379, R01CA204254 and CPRIT RP240289.}
}
```