Instructions to use juyil/spatial-8act with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juyil/spatial-8act with Transformers:
# Load model directly from transformers import AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("juyil/spatial-8act", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
library_name: transformers
pipeline_tag: robotics
---
This repository contains the checkpoint trained on spatial LIBERO task suite, as described in [VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting](https://arxiv.org/abs/2507.05116).
The official code is available in the [GitHub Repository](https://github.com/LukeLIN-web/VOTE).
## Citation
If you use our code in your work, please cite [our paper](https://arxiv.org/abs/2507.05116):
```bibtex
@misc{lin2025votevisionlanguageactionoptimizationtrajectory,
title={VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting},
author={Juyi Lin and Amir Taherin and Arash Akbari and Arman Akbari and Lei Lu and Guangyu Chen and Taskin Padir and Xiaomeng Yang and Weiwei Chen and Yiqian Li and Xue Lin and David Kaeli and Pu Zhao and Yanzhi Wang},
year={2025},
eprint={2507.05116},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.05116},
}
``` |