  Target following in dynamic pedestrian environments is an important task for
mobile robots. However, it is challenging to keep tracking the target while
avoiding collisions in crowded environments, especially with only one robot. In
this paper, we propose a multi-agent method for an arbitrary number of robots
to follow the target in a socially-aware manner using only 2D laser scans. The
multi-agent following problem is tackled by utilizing the complementary
strengths of both reinforcement learning and potential field, in which the
reinforcement learning part handles local interactions while navigating to the
goals assigned by the potential field. Specifically, with the help of laser
scans in obstacle map representation, the learning-based policy can help the
robots avoid collisions with both static obstacles and dynamic obstacles like
pedestrians in advance, namely socially aware. While the formation control and
goal assignment for each robot is obtained from a target-centered potential
field constructed using aggregated state information from all the following
robots. Experiments are conducted in multiple settings, including random
obstacle distributions and different numbers of robots. Results show that our
method works successfully in unseen dynamic environments. The robots can follow
the target in a socially compliant manner with only 2D laser scans.
