Expressing Robot Incapability
Open accessAbstract
Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future.
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While some robot failures can be detected through changes in the robot's behavior or posture, changes in the robot's physicality, or changes in the user's behavior, other issues produce no obvious symptoms.
“While some robot failures can be detected through changes in the robot's behavior or posture (e.g., Takayama et al., 2011; Kwon et al., 2018), changes in the robot's physicality (e.g., a wheel falling off), or changes in the user's behavior (see section Act), other issues (e.g., missing data) produce no obvious symptoms.”
From Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018) · cites this paper as Kwon et al., 2018
The use of motions expressing physical limitations increased positive evaluations of the robot and willingness to collaborate.
“The use of these motions was found to increase positive evaluations of the robot and willingness to collaborate.”
From Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018) · cites this paper as Kwon et al. (2018)
Cites
Impact of robot failures and feedback on real-time trust
Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., & Yanco, H. (2013) · 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2013.6483596 · Cited by 234 · see citing works
The effect of a pratfall on increasing interpersonal attractiveness
Aronson, E., Willerman, B., & Floyd, J. (1966) · Psychonomic Science · doi:10.3758/bf03342263 · Cited by 218 · see citing works
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Honig, S., & Oron-Gilad, T. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00861 · Cited by 338 · see citing works
Automated rationale generation: a technique for explainable AI and its effects on human perceptions
Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., & Riedl, M. O. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302316 · Cited by 210 · see citing works
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., & Riedl, M. O. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642474 · Cited by 123 · see citing works
Establishing Appropriate Trust via Critical States
Huang, S. H., Bhatia, K., Abbeel, P., & Dragan, A. D. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593649 · Cited by 94 · see citing works
Self-citationDesigning Human-Agent Collaborations: Commitment, responsiveness, and support
Cila, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517500 · Cited by 65 · see citing works
- Authors
- Kwon, M., Huang, S. H., & Dragan, A. D.
- Affiliations
- Cornell University (US)
- University of California, Berkeley (US)
- ORCID
- Kwon, Minae
- Venue
- Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2018-02-26
- Pages
- 87-95
- OpenAlex
- W2789347656
- Cited by
- 123
- References
- 2
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 27 · 2023: 7 · 2022: 14 · 2021: 21 · 2020: 14 · 2019: 11 · 2018: 5
- FWCI
- 22.77
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Hong Kong University of Science and Technology
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Keywords
- trajectory optimization, similarity measures, human-robot interaction, robot communication, user perception of robots, human-robot collaboration
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