Online Context Learning for Socially-compliant Navigation

• URL: http://arxiv.org/abs/2406.11495v1
• Date: Mon, 17 Jun 2024 12:59:13 GMT
• Title: Online Context Learning for Socially-compliant Navigation
• Authors: Iaroslav Okunevich, Alexandre Lombard, Tomas Krajnik, Yassine Ruichek, Zhi Yan,
• Abstract summary: This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones.
• Score: 49.609656402450746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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