Expressing Robot Incapability
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2020: 1; 2021: 3; 2022: 3; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
12 papers.
- 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
- 2024GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignmentcited by 16
- 2023Aligning Robot and Human Representationscited by 11
- 2021Human-AI Symbiosis: A Survey of Current Approachescited by 8
- 2022Teaching Robots to Span the Space of Functional Expressive Motioncited by 6
- 2021Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaborationcited by 4
- 2022A Mental-Model Centric Landscape of Human-AI Symbiosiscited by 3
- 2022"Guess what I'm doing": Extending legibility to sequential decision taskscited by 2
- 2023Towards Reconciling Usability and Usefulness of Explainable AI Methodologiescited by 2
- 2020Investigating the Effects of Robot Engagement Communication on Learning from Demonstrationcited by 0
- 2025Rude Humans and Vengeful Robots: Examining Human Perceptions of Robot Retaliatory Intentions in Professional Settingscited by 0
- 2026Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanationscited by 0
- 2026Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations21 May 2026
- 2025Rude Humans and Vengeful Robots: Examining Human Perceptions of Robot Retaliatory Intentions in Professional Settings21 Mar 2025
- 2024GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment17 Mar 2024
- 2023Aligning Robot and Human Representations3 Feb 2023
- 2023Towards Reconciling Usability and Usefulness of Explainable AI Methodologies13 Jan 2023
- 2022"Guess what I'm doing": Extending legibility to sequential decision tasks19 Sep 2022
- 2022Teaching Robots to Span the Space of Functional Expressive Motion4 Mar 2022
- 2022A Mental-Model Centric Landscape of Human-AI Symbiosis18 Feb 2022
- 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanations28 Jul 2021
- 2021Human-AI Symbiosis: A Survey of Current Approaches18 Mar 2021
- 2021Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration6 Mar 2021
- 2020Investigating the Effects of Robot Engagement Communication on Learning from Demonstration3 May 2020