SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning
Jianpeng Yao, Xiaopan Zhang, Yu Xia, Zejin Wang, Amit K., Roy-Chowdhury, Jiachen Li

TL;DR
SoNIC introduces a novel integration of adaptive conformal inference with constrained reinforcement learning to enhance safety and robustness in social robot navigation, significantly reducing collisions and improving success rates.
Contribution
It is the first to combine ACI with CRL for safe social navigation, improving safety and robustness in complex environments.
Findings
Achieved 96.93% success rate on CrowdNav benchmark
Reduced collisions by 4.5 times compared to previous methods
Demonstrated robust, socially polite navigation in real robot experiments
Abstract
Reinforcement learning (RL) enables social robots to generate trajectories without relying on human-designed rules or interventions, making it generally more effective than rule-based systems in adapting to complex, dynamic real-world scenarios. However, social navigation is a safety-critical task that requires robots to avoid collisions with pedestrians, whereas existing RL-based solutions often fall short of ensuring safety in complex environments. In this paper, we propose SoNIC, which to the best of our knowledge is the first algorithm that integrates adaptive conformal inference (ACI) with constrained reinforcement learning (CRL) to enable safe policy learning for social navigation. Specifically, our method not only augments RL observations with ACI-generated nonconformity scores, which inform the agent of the quantified uncertainty but also employs these uncertainty estimates to…
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