An Empirical Investigation of Intelligent Disobedience for Mitigating Human Error in Collaborative Teleoperation

Human-induced errors—such as slips, lapses, and mistakes—are natural and often unavoidable, posing significant safety risks in teleoperation, particularly in high-risk, dynamic environments like underwater operations. While previous work has emphasised system-level enhancements, the proactive mitigation of human-induced errors through empirical evaluation remains underexplored. This work presents the first empirical investigation of Intelligent Disobedience (ID) as a collaborative strategy for mitigating human errors in teleoperation. A semi-controlled, game-based Wizard-of-Oz study with 40 participants performing an underwater navigation task was conducted. Two ID strategies were evaluated: one with automatic robot mitigation and another with user-mediated robot mitigation following the disobedient intervention. Using a mixed-methods approach that triangulated quantitative performance metrics with qualitative insights from semi-structured interviews, the study examined the effects of these strategies on operator performance, acceptance, and error-mitigation outcomes across different error types. The results revealed no detectable performance degradation under ID and emphasised that human error types differ in their characteristics, necessitating the adaptation of ID intervention strategies. These findings underscore the importance of context-adaptive approaches when integrating ID into collaborative teleoperation systems.