A Tale of Two Suggestions: Action and Diagnosis Recommendations for Responding to Robot Failure

Abstract

Robots operating without close human supervision might need to rely on a remote call center of operators for assistance in the event of a failure. In this work, we investigate the effects of providing decision support through diagnosis suggestions, as feedback, and action recommendations, as feedforward, to the human operators. We conduct a 10-condition user study involving 200 participants on Amazon Mechanical Turk to evaluate the effects of providing noisy and noise-free diagnosis suggestions and/or action recommendations to operators. We find that although action recommendations (feedforward) have a greater effect on successful error resolution than diagnosis information (feedback), the feedback likely helps ameliorate the deleterious effects of noise. Therefore, we find that error recovery interfaces should display both diagnosis and action recommendations for maximum effectiveness.