Flexible Robot Error Detection Using Natural Human Responses for Effective HRI

摘要

Robot errors during human-robot interaction are inescapable; they can occur during any task and do not necessarily fit human expectations. When left unmanaged, robot errors harm task performance and user trust, resulting in user unwillingness to work with a robot. Existing error detection techniques often specialize in specific tasks or error types, using task or error specific information for robust management and so may lack the versatility to appropriately address robot errors across tasks and error types. To achieve flexible error detection, my work leverages natural human responses to robot errors in physical HRI for error detection across task, scenario, and error type in support of effective robot error management.