Human Experiences in Teaching Robots: Understanding Agent Expressivity and Learning Effects through a Virtual Robot Arm

Abstract

Robots are being taught by increasingly broader populations of people who provide training data for machine learning algorithms. Many studies over the past decade have begun demonstrating reproducible robot teaching methodologies and have highlighted benefits in human-robot interaction (HRI). However, there have been few investigations about what it is like for the people teaching these robots. In this study, we consider how teaching a skill to a robot arm, performing a reaching task (as opposed to observing the robot self-learning), influences a user's emotional experience and perceptions of the robot. In a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{2\mathrm{x}2}$</tex> experiment <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{(N=160)}$</tex> , we varied the agent's learning technique (user reinforcement feedback or robot self-learning) and expressiveness (static agent face or performance-based valence expression with head following), using an online WebGL virtual environment to enable remote HRI. Our results demonstrate that users experience significantly more trust, believability, and emotional response when teaching the robot than when observing it learning, which can be amplified with agent expressiveness.