LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
<|reference_start|>LLaRA: Supercharging Robot Learning Data for Vision-Language Policy: LLMs with visual inputs, i.e., Vision Language Models (VLMs), have the capacity to process state information as visual-textual prompts and respond with policy decisions in text. We propose LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as conversations and provides improved action outputs when trained with auxiliary data that complements policy learning. We first introduce an automated pipeline to generate conversation-style instruction tuning data from existing behavior cloning data. Then we enrich the dataset in a self-supervised fashion by formulating six auxiliary tasks. A VLM finetuned with the resulting collection of datasets can generate meaningful robot action policy decisions. Our experiments across multiple simulated and real-world environments demonstrate the state-of-the-art performance of the proposed LLaRA framework. The code, datasets, and pretrained models are available at https://github.com/LostXine/LLaRA.<|reference_end|>
@article{li2024llara:,
    title={LLaRA: Supercharging Robot Learning Data for Vision-Language Policy},
    author={Xiang Li, Cristina Mata, Jongwoo Park, Kumara Kahatapitiya, Yoo Sung
  Jang, Jinghuan Shang, Kanchana Ranasinghe, Ryan Burgert, Mu Cai, Yong Jae
  Lee, Michael S. Ryoo},
    journal={arXiv preprint arXiv:2406.20095},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.20095},
    primaryClass={cs.RO cs.AI cs.CL cs.CV cs.LG}
}