Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

Abstract-- Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. Another problem stemming increasingly enabling robots to work in environments that form handcrafted rewards is reward exploitation, that is, necessitate human-robot interaction (HRI). Delivery robots robots learn to achieve high rewards via some undesired and around university campuses, guide robots in shopping malls, unnatural action that impairs human comfort. On the other elder care robots at nursing homes, and other such applications hand, IRL methods, where a policy or reward is learned from all require robots to perform socially aware navigation human demonstrations, can avoid reward engineering and in human-rich environments, wherein the robots must not exploitation and allow experts to introduce human insights only consider how to complete navigation tasks successfully and comfort into robot policy.