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 this https URL

该研究介绍了一种在线环境学习方法，旨在使机器人适应新的社交环境，并采用深度增强学习和在线机器人学习方法来提高机器人社交导航性能。