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

Yifan Xu; Theodor Chakhachiro; Tribhi Kathuria; Maani Ghaffari

ICRA 2023poster3 citations

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

Yifan Xu, Theodor Chakhachiro, Tribhi Kathuria, Maani Ghaffari

Abstract

This work proposes a novel framework for socially-aware robot navigation in dynamic, crowded environments using a Deep Inverse Reinforcement Learning. To address the social navigation problem, our multi-modal learning based planner explicitly considers social interaction factors, as well as social-awareness factors, into the DIRL 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.

BibTeX

@inproceedings{icra2023_solotdirlsociall,
  title = {SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning},
  author = {Yifan Xu and Theodor Chakhachiro and Tribhi Kathuria and Maani Ghaffari},
  booktitle = {ICRA 2023},
  year = {2023}
}