Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human Teachers
Authors: 
Shuai Ma, Mingfei Sun, Xiaojuan MaAuthors Info & Claims
Shuai Ma, Mingfei Sun, Xiaojuan MaAuthors Info & ClaimsArticle No.: 70, Pages 1 - 48
Abstract
Robot Learning from Demonstration (RLfD) allows non-expert users to teach a robot new skills or tasks directly through demonstrations. Although modeled after human–human learning and teaching, existing RLfD methods make robots act as passive observers without the feedback of their learning statuses in the demonstration gathering stage. To facilitate a more transparent teaching process, we propose two mechanisms of Learning Engagement, Z2O-Mode and D2O-Mode, to dynamically adapt robots’ attentional and behavioral engagement expressions to their actual learning status. Through an online user experiment with 48 participants, we find that, compared with two baselines, the two kinds of Learning Engagement can lead to users’ more accurate mental models of the robot’s learning progress, more positive perceptions of the robot, and better teaching experience. Finally, we provide implications for leveraging engagement expression to facilitate transparent human-AI (robot) communication based on our key findings.
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- Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human Teachers
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October 2023
593 pages
ISSN:1073-0516
EISSN:1557-7325
DOI:10.1145/3623487
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Publication History
Published: 23 September 2023
Online AM: 19 November 2022
Accepted: 05 August 2022
Revised: 31 May 2022
Received: 30 June 2021
Published in TOCHI Volume 30, Issue 5
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