Online Context Learning for Socially Compliant Navigation

TL;DR

This paper presents an online context learning approach for socially compliant robot navigation, combining deep reinforcement learning with online adaptation to improve social behavior in diverse environments.

Contribution

It introduces a two-layer online learning framework that enables robots to adapt to new social contexts during deployment, enhancing long-term and cross-environment navigation.

Findings

Outperforms state-of-the-art methods in community-wide simulations.

Improves navigation performance by 8% in challenging scenarios.

Demonstrates effective online adaptation to social environments.

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 are publicly available at https://github.com/Nedzhaken/SOCSARL-OL.