Effective Human-Robot Collaboration via Generalized Robot Error Management Using Natural Human Responses

Abstract

Robot errors during human-robot interaction will be unavoidable. Their impact on the collaboration is dependent on the human's perception of it and how timely it is detected and recovered from. Prior work in robot error management often uses task or error specific information for reliable management and so is not generalizable to other contexts or error types. To achieve generalized error management, one approach is the use of human response as input. My PhD thesis will focus on enabling effective human-robot interaction through leveraging users' natural multimodal response to errors to detect, classify, mitigate, and recover from them. This extended abstract details my past, current, and future work towards this goal.