---
license: apache-2.0
datasets:
- wentao-yuan/robopoint-data
base_model:
- meta-llama/Llama-2-7b-chat-hf
---
# RoboPoint-v1-Llama2-7B-LoRA
RoboPoint is an open-source vision-language model instruction-tuned on a mix of robotics and VQA data. Given an image with language instructions, it outputs precise action guidance as points.

## Primary Use Cases
RoboPoint can predict spatial affordances—where actions should be taken in relation to other entities—based on instructions. For example, it can identify free space on a shelf in front of the rightmost object.

## Model Details
This model was fine-tuned using [LoRA](https://arxiv.org/abs/2106.09685) from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and has 7 billion parameters.

## Date
This model was trained in June 2024.

## Resources for More Information

- Paper: https://arxiv.org/pdf/2406.10721
- Code: https://github.com/wentaoyuan/RoboPoint
- Website: https://robo-point.github.io

## Training dataset
See [wentao-yuan/robopoint-data](https://huggingface.co/datasets/wentao-yuan/robopoint-data).

## Citation
If you find our work helpful, please consider citing our paper.
```
@article{yuan2024robopoint,
  title={RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics},
  author={Yuan, Wentao and Duan, Jiafei and Blukis, Valts and Pumacay, Wilbert and Krishna, Ranjay and Murali, Adithyavairavan and Mousavian, Arsalan and Fox, Dieter},
  journal={arXiv preprint arXiv:2406.10721},
  year={2024}
}
```