  Mobility in an effective and socially-compliant manner is an essential yet
challenging task for robots operating in crowded spaces. Recent works have
shown the power of deep reinforcement learning techniques to learn socially
cooperative policies. However, their cooperation ability deteriorates as the
crowd grows since they typically relax the problem as a one-way Human-Robot
interaction problem. In this work, we want to go beyond first-order Human-Robot
interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose
to (i) rethink pairwise interactions with a self-attention mechanism, and (ii)
jointly model Human-Robot as well as Human-Human interactions in the deep
reinforcement learning framework. Our model captures the Human-Human
interactions occurring in dense crowds that indirectly affects the robot's
anticipation capability. Our proposed attentive pooling mechanism learns the
collective importance of neighboring humans with respect to their future
states. Various experiments demonstrate that our model can anticipate human
dynamics and navigate in crowds with time efficiency, outperforming
state-of-the-art methods.
