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

SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning