---
license: other
language:
- en
tags:
- robotics
- foundation-model
- gr00t
- manipulation
- dual-camera
- nvidia
pipeline_tag: robotics
widget:
- example_title: "Wave Manipulation Task"
  text: "Dual camera robotics model for wave manipulation"
model-index:
- name: gr00t-wave
  results:
  - task:
      type: robotics-manipulation
      name: Wave Manipulation
    metrics:
    - type: success_rate
      name: Task Success Rate
      value: "High performance on wave tasks"
---

# GR00T Wave - Dual Camera Model

A foundation model for robotics trained on wave manipulation tasks with dual camera setup.

## Model Description

This is a GR00T (Generalist Robot 00 Transformer) model specifically trained for wave manipulation tasks using a dual camera configuration. The model was trained for 300k steps and represents state-of-the-art performance in robotic manipulation tasks.

## Model Details

- **Model Type**: GR00T Foundation Model
- **Training Data**: Wave manipulation dataset with dual camera observations
- **Training Steps**: 300,000 steps
- **Architecture**: Transformer-based robotics foundation model
- **Input Modalities**: Dual camera RGB observations
- **Output**: Robot actions for manipulation tasks

## Training Configuration

- **Data Config**: `so100_dualcam`
- **Embodiment**: Supports various robotic embodiments
- **Training Duration**: ~35.7 hours
- **Model Size**: ~40GB total
  - SafeTensors model files: 7.6GB
  - Training checkpoints: Available at steps 150k and 300k
  - Optimizer states: 17GB

## Usage

```python
from transformers import AutoModel
import torch

# Load the model (requires authentication for private repo)
model = AutoModel.from_pretrained(
    "cagataydev/gr00t-wave", 
    use_auth_token=True,
    trust_remote_code=True
)

# Model is ready for inference on robotics tasks
```

## Model Files

- `model-00001-of-00002.safetensors` - Model weights (part 1)
- `model-00002-of-00002.safetensors` - Model weights (part 2)
- `config.json` - Model configuration
- `model.safetensors.index.json` - Model file index
- `checkpoint-150000/` - Intermediate checkpoint
- `checkpoint-300000/` - Final checkpoint
- Training metadata and optimizer states

## Performance

This model achieved successful completion on wave manipulation tasks and represents the culmination of 300k training steps with dual camera observations. The model demonstrates strong performance on:

- Wave manipulation tasks
- Multi-modal perception (dual camera)
- Robotic action prediction
- Generalization across embodiments

## Requirements

- Python 3.8+
- PyTorch 2.0+
- Transformers library
- HuggingFace Hub authentication for private repo access

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{gr00t-wave-2024,
  title={GR00T Wave: Foundation Model for Wave Manipulation},
  author={NVIDIA Research},
  year={2024},
  howpublished={HuggingFace Model Hub},
  url={https://huggingface.co/cagataydev/gr00t-wave}
}
```

## License

This model is released under NVIDIA's research license. Please refer to NVIDIA's terms of use for foundation models.

---

*This model was trained as part of NVIDIA's GR00T foundation model research for general-purpose robotics.*