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 are publicly available atthis https URL.
View on arXiv@article{okunevich2025_2406.11495,
title={ Online Context Learning for Socially Compliant Navigation },
author={ Iaroslav Okunevich and Alexandre Lombard and Tomas Krajnik and Yassine Ruichek and Zhi Yan },
journal={arXiv preprint arXiv:2406.11495},
year={ 2025 }
}