README.md

---
license: apache-2.0
tags:
- robotics
- embodied-ai
- fruit-manipulation
- gr00t
- nvidia
- pytorch
- fine-tuned
datasets:
- aaronsu11/so101_fruit
library_name: transformers
pipeline_tag: robotics
base_model: nvidia/GR00T-N1.5-3B
model_type: gr00t
language:
- en
---

# GR00T Fruit Manipulation Model

## Model Description

This is a GR00T model fine-tuned for fruit manipulation tasks. The model has been trained for 6,000 steps on fruit handling and manipulation scenarios.

## Training Details

- **Model Architecture**: GR00T-N1.5-3B
- **Training Steps**: 6,000
- **Training Duration**: ~2 hours
- **Batch Size**: 32
- **Data Configuration**: so100_dualcam
- **Embodiment**: New embodiment configuration

## Dataset

This model was trained using the **so101_fruit** dataset, which contains fruit manipulation demonstrations.

**Original Dataset Source**: [https://huggingface.co/datasets/aaronsu11/so101_fruit](https://huggingface.co/datasets/aaronsu11/so101_fruit)

Please cite the original dataset when using this model:
```
@dataset{aaronsu11_so101_fruit,
  title={SO101 Fruit Dataset},
  author={aaronsu11},
  url={https://huggingface.co/datasets/aaronsu11/so101_fruit},
  year={2024}
}
```

## Capabilities

This model is designed for:
- Fruit handling and manipulation tasks
- Object grasping and placement
- Robotic manipulation in kitchen/food preparation scenarios

## Usage

Load the model using the standard GR00T inference pipeline:

```python
# Example usage with GR00T inference
from gr00t_inference import GR00TModel

model = GR00TModel.from_pretrained("cagataydev/gr00t-fruit-6k")
# Use for fruit manipulation tasks
```

## Model Files

The repository contains:
- `model-00001-of-00002.safetensors` & `model-00002-of-00002.safetensors`: Model weights
- `config.json`: Model configuration
- `model.safetensors.index.json`: Model index
- `trainer_state.json`: Training state information
- `training_args.bin`: Training arguments

## Training Infrastructure

- **Platform**: Ubuntu
- **Compute**: Single GPU
- **Framework**: GR00T training pipeline
- **Checkpoints**: Saved every 2,000 steps

## License

Please refer to the original dataset license and GR00T model license for usage terms.

## Acknowledgments

Special thanks to the creators of the original SO101 Fruit dataset for providing high-quality training data for robotic manipulation research.