Robot Errors in Proximate HRI

Abstract

Advancements within human–robot interaction generate increasing opportunities for proximate, goal-directed joint action (GDJA). However, robot errors are common and researchers must determine how to mitigate them. In this article, we examine how expectations for robot functionality affect people’s perceptions of robot reliability and trust for a robot that makes errors. Here 35 participants ( n = 35) performed a collaborative banner-hanging task with an autonomous mobile manipulator (Toyota HSR). Each participant received either a low- or high-functionality framing for the robot. We then measured how participants perceived the robot’s reliability and trust prior to, during, and after interaction. Functionality framing changed how robot errors affected participant experiences of robot behavior. People with low expectations experienced positive changes in reliability and trust after interacting with the robot, while those with high expectations experienced a negative change in reliability and no change in trust. The low-expectation group also showed greater trust recovery following the robot’s first error compared to the high group. Our findings inform human–robot teaming through: (1) identifying robot presentation factors that can be employed to facilitate trust calibration and (2) establishing the effects of framing, functionality, and the interactions between them to improve dynamic models of human–robot teaming.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 31, 2020
Source ID
10.1145/3380783

Entities

People

  • Akanimoh Adeleye
  • Auriel Washburn
  • Laurel D. Riek
  • Thomas An

Organizations

  • Air Force Office of Scientific Research
  • National Science Foundation
  • University of California, San Diego

Tags

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Autonomy - Human-Robot Interaction