  This work proposes a new framework for a socially-aware dynamic local planner
in crowded environments by building on the recently proposed Trajectory-ranked
Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the
social navigation problem, our multi-modal learning planner explicitly
considers social interaction factors, as well as social-awareness factors into
T-MEDIRL pipeline to learn a reward function from human demonstrations.
Moreover, we propose a novel trajectory ranking score using the sudden velocity
change of pedestrians around the robot to address the sub-optimality in human
demonstrations. Our evaluation shows that this method can successfully make a
robot navigate in a crowded social environment and outperforms the state-of-art
social navigation methods in terms of the success rate, navigation time, and
invasion rate.
