Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling

Shared autonomy functions as a flexible framework that empowers robots to operate across a spectrum of autonomy levels, allowing for efficient task execution with minimal human oversight. However, humans might be intimidated by the autonomous decision-making capabilities of robots due to perceived risks and a lack of trust. This paper proposed a trust-preserved shared autonomy strategy that grants robots to seamlessly adjust their autonomy level, striving to optimize team performance and enhance their acceptance among human collaborators. By enhancing the Relational Event Modeling framework with Bayesian learning techniques, this paper enables dynamic inference of human trust based solely on time-stamped relational events within human-robot teams. Adopting a longitudinal perspective on trust development and calibration in human-robot teams, the proposed shared autonomy strategy warrants robots to preserve human trust by not only passively adapting to it but also actively participating in trust repair when violations occur. We validate the effectiveness of the proposed approach through a user study on human-robot collaborative search and rescue scenarios. The objective and subjective evaluations demonstrate its merits over teleoperation on both task execution and user acceptability.