It's not what you think: shaping beliefs about a robot to influence a teleoperator's expectations and behavior

Front Robot AI. 2023 Dec 21:10:1271337. doi: 10.3389/frobt.2023.1271337. eCollection 2023.

Abstract

In this paper we present a novel design approach for shaping a teleoperator's expectations and behaviors when teleoperating a robot. Just as how people may drive a car differently based on their expectations of it (e.g., the brakes may be poor), we assert that teleoperators may likewise operate a robot differently based on expectations of robot capability and robustness. We present 3 novel interaction designs that proactively shape teleoperator perceptions, and the results from formal studies that demonstrate that these techniques do indeed shape operator perceptions, and in some cases, measures of driving behavior such as changes in collisions. Our methods shape operator perceptions of a robot's speed, weight, or overall safety, designed to encourage them to drive more safely. This approach shows promise as an avenue for improving teleoperator effectiveness without requiring changes to a robot, novel sensors, algorithms, or other functionality.

Keywords: human-robot interaction; perception; priming; teleoperation; user experience.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.