SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
This work addresses the problem of enabling robots to navigate safely and efficiently in crowded social environments, representing an incremental improvement over existing social navigation methods.
The paper tackled social navigation in crowded environments by proposing a multi-modal learning planner that incorporates social interaction and awareness factors into a deep inverse reinforcement learning pipeline, achieving improved success rate, navigation time, and invasion rate compared to state-of-the-art methods.
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.