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

TL;DR

This paper introduces SoLo T-DIRL, a socially-aware local planner that learns from human demonstrations to navigate crowded environments effectively, outperforming existing methods in success rate and social comfort.

Contribution

It develops a novel trajectory ranking score based on pedestrian velocity changes and integrates social interaction factors into T-MEDIRL for improved social navigation.

Findings

Outperforms state-of-the-art social navigation methods

Achieves higher success rate and lower invasion rate

Reduces navigation time in crowded environments

Abstract

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.