  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.
