Authors: Jianpeng Yao, Xiaopan Zhang, Yu Xia, Zejin Wang, Amit K. Roy-Chowdhury, Jiachen Li
Abstract: Reinforcement Learning (RL) has enabled social robots to generate
trajectories without human-designed rules or interventions, which makes it more
effective than hard-coded systems for generalizing to complex real-world
scenarios. However, social navigation is a safety-critical task that requires
robots to avoid collisions with pedestrians while previous RL-based solutions
fall short in safety performance in complex environments. To enhance the safety
of RL policies, to the best of our knowledge, we propose the first algorithm,
SoNIC, that integrates adaptive conformal inference (ACI) with constrained
reinforcement learning (CRL) to learn safe policies for social navigation. More
specifically, our method augments RL observations with ACI-generated
nonconformity scores and provides explicit guidance for agents to leverage the
uncertainty metrics to avoid safety-critical areas by incorporating safety
constraints with spatial relaxation. Our method outperforms state-of-the-art
baselines in terms of both safety and adherence to social norms by a large
margin and demonstrates much stronger robustness to out-of-distribution
scenarios. Our code and video demos are available on our project website:
https://sonic-social-nav.github.io/.
Source: http://arxiv.org/abs/2407.17460v1
