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Cited by 11Social Robot Interaction and HRIExplainable Artificial Intelligence (XAI)Robot Manipulation and Learning

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Communicating Missing Causal Information to Explain a Robot’s Past Behavior

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Han, Zhao, Yanco, Holly·2023·ACM Transactions on Human-Robot Interaction
DOI 10.1145/3568024
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Abstract

Robots need to explain their behavior to gain trust. Existing research has focused on explaining a robot’s current behavior, yet it remains unknown yet challenging how to provide explanations of past actions in an environment that might change after a robot’s actions, leading to critical missing causal information due to moved objects. We conducted an experiment (N = 665) investigating how a robot could help participants infer the missing causal information by replaying the past behavior physically, using verbal explanations, and projecting visual information onto the environment. Participants watched videos of the robot replaying its completion of an integrated mobile kitting task. During the replay, the objects are already gone, so participants needed to infer where an object was picked, where a ground obstacle had been, and where the object was placed. Based on the results, we recommend combining physical replay with speech and projection indicators (Replay-Project-Say) to help infer all the missing causal information (picking, navigation, and placement) from the robot’s past actions. This condition had the best outcome in both task-based—effectiveness, efficiency, and confidence—and team-based metrics—workload and trust. If one’s focus is efficiency, then we recommend projection markers for navigation inferences and verbal markers for placing inferences.

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  • The System Causability Scale and the Muir Trust Scale are widely adopted in human robot interaction and robotics and rely on predictability, reliability, competence and trust.

    “Examples include the System Causability Scale [123] or the Muir Trust Scale [124], which are widely adopted in human robot interaction and robotics and rely on predictability ( To what extent the robot behavior [the output of the AI-based system] can be predicted from moment to moment? ), reliability ( To what extent can you count on the system to do its job? ), competence ( What degree of faith”

    From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [124]

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Metadata

Authors
Han, Z., & Yanco, H.
Affiliations
  • University of Massachusetts Lowell, USA
  • University of Massachusetts Lowell (US)
ORCID
Venue
ACM Transactions on Human-Robot Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2023-02-15
Volume
12
Issue
1
Pages
1-45
ISSN
2573-9522 (ISSN-L)
DOI
10.1145/3568024
OpenAlex
W4307985155
Cited by
11
References
5
Citations by year
2026: 3 · 2025: 3 · 2024: 4 · 2023: 1
FWCI
1.89
Type
journal article
Language
en
Open access
Licence
Locations
ACM Transactions on Human-Robot Interaction (publishedVersion, cc-by-nc-nd)
Funders
Office of Naval Research (N00014-18-1-2503)
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Keywords
verbal explanation, trust in robots, human-robot teams, workload

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