Distill: Uncovering the True Intent behind Human-Robot Communication
A new approach called Distill helps robots better understand human intent by simplifying and generalizing task specifications. A crowdsourcing study shows its effectiveness.
[2605.14262] Distill: Uncovering the True Intent behind Human-Robot Communication
[Submitted on 14 May 2026]
Title:Distill: Uncovering the True Intent behind Human-Robot Communication
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Abstract:As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for human-robot communication interfaces. Given a task specification provided by the user, Distill (1) removes unnecessary steps; (2) generalizes the meaning behind individual steps; and (3) relaxes ordering constraints between steps. We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.
Comments: 17 pages
Subjects:
Robotics (cs.RO); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.14262 [cs.RO]
(or arXiv:2605.14262v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.14262
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3800645.3813000
DOI(s) linking to related resources
Submission history
From: Ting Li [view email] [v1] Thu, 14 May 2026 02:05:49 UTC (4,285 KB)
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