  This work presents a deep reinforcement learning framework for interactive
navigation in a crowded place. Our proposed approach, Learning to Balance (L2B)
framework enables mobile robot agents to steer safely towards their
destinations by avoiding collisions with a crowd, while actively clearing a
path by asking nearby pedestrians to make room, if necessary, to keep their
travel efficient. We observe that the safety and efficiency requirements in
crowd-aware navigation have a trade-off in the presence of social dilemmas
between the agent and the crowd. On the one hand, intervening in pedestrian
paths too much to achieve instant efficiency will result in collapsing a
natural crowd flow and may eventually put everyone, including the self, at risk
of collisions. On the other hand, keeping in silence to avoid every single
collision will lead to the agent's inefficient travel. With this observation,
our L2B framework augments the reward function used in learning an interactive
navigation policy to penalize frequent active path clearing and passive
collision avoidance, which substantially improves the balance of the
safety-efficiency trade-off. We evaluate our L2B framework in a challenging
crowd simulation and demonstrate its superiority, in terms of both navigation
success and collision rate, over a state-of-the-art navigation approach.
