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

Siddhartha Banerjee, Matthew Gombolay, Sonia Chernova

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.