# Expressing Robot Incapability

arXiv: https://arxiv.org/abs/1810.08167 · HTML: https://mlsanity.com/paper/1810.08167/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
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- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Human-AI Symbiosis: A Survey of Current Approaches](https://mlsanity.com/paper/2103.09990/) (2021)
- [Teaching Robots to Span the Space of Functional Expressive Motion](https://mlsanity.com/paper/2203.02091/) (2022)
- [Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration](https://mlsanity.com/paper/2103.04077/) (2021)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- ["Guess what I'm doing": Extending legibility to sequential decision tasks](https://mlsanity.com/paper/2209.09141/) (2022)
- [Towards Reconciling Usability and Usefulness of Explainable AI Methodologies](https://mlsanity.com/paper/2301.05347/) (2023)
- [Investigating the Effects of Robot Engagement Communication on Learning from Demonstration](https://mlsanity.com/paper/2005.01020/) (2020)
- [Robots that Suggest Safe Alternatives](https://mlsanity.com/paper/2409.09883/) (2024)
- [Rude Humans and Vengeful Robots: Examining Human Perceptions of Robot Retaliatory Intentions in Professional Settings](https://mlsanity.com/paper/2503.16932/) (2025)
- [Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations](https://mlsanity.com/paper/2605.22986/) (2026)

## 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.