SRLM: Human-in-Loop Interactive Social Robot Navigation with Large Language Model and Deep Reinforcement Learning
<|reference_start|>SRLM: Human-in-Loop Interactive Social Robot Navigation with Large Language Model and Deep Reinforcement Learning: An interactive social robotic assistant must provide services in complex and crowded spaces while adapting its behavior based on real-time human language commands or feedback. In this paper, we propose a novel hybrid approach called Social Robot Planner (SRLM), which integrates Large Language Models (LLM) and Deep Reinforcement Learning (DRL) to navigate through human-filled public spaces and provide multiple social services. SRLM infers global planning from human-in-loop commands in real-time, and encodes social information into a LLM-based large navigation model (LNM) for low-level motion execution. Moreover, a DRL-based planner is designed to maintain benchmarking performance, which is blended with LNM by a large feedback model (LFM) to address the instability of current text and LLM-driven LNM. Finally, SRLM demonstrates outstanding performance in extensive experiments. More details about this work are available at: https://sites.google.com/view/navi-srlm<|reference_end|>
@article{wang2024srlm:,
    title={SRLM: Human-in-Loop Interactive Social Robot Navigation with Large
  Language Model and Deep Reinforcement Learning},
    author={Weizheng Wang, Le Mao, Ruiqi Wang, and Byung-Cheol Min},
    journal={arXiv preprint arXiv:2403.15648},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15648},
    primaryClass={cs.RO cs.AI}
}