Online Context Learning for Socially-compliant Navigation
<|reference_start|>Online Context Learning for Socially-compliant Navigation: Robot social navigation needs to adapt to different human factors and environmental contexts. However, since these factors and contexts are difficult to predict and cannot be exhaustively enumerated, traditional learning-based methods have difficulty in ensuring the social attributes of robots in long-term and cross-environment deployments. This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. The proposed method adopts a two-layer structure. The bottom layer is built using a deep reinforcement learning-based method to ensure the output of basic robot navigation commands. The upper layer is implemented using an online robot learning-based method to socialize the control commands suggested by the bottom layer. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones. Experimental results in the most challenging scenarios show that our method improves the performance of the state-of-the-art by 8%. The source code of the proposed method, the data used, and the tools for the per-training step will be publicly available at https://github.com/Nedzhaken/SOCSARL-OL.<|reference_end|>
@article{okunevich2024online,
    title={Online Context Learning for Socially-compliant Navigation},
    author={Iaroslav Okunevich, Alexandre Lombard, Tomas Krajnik, Yassine Ruichek,
  Zhi Yan},
    journal={arXiv preprint arXiv:2406.11495},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11495},
    primaryClass={cs.RO cs.AI}
}