CoFrame: A System for Training Novice Cobot Programmers

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications in which they are currently deployed, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and processes they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system and evaluated how automated expert feedback supports novice troubleshooting and user perception. We evaluated our system with novice and expert users to understand their experience and expectations for robot programming.

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Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2026-10-29
DOI
https://doi.org/10.1145/3860630
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
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article

CoFrame: A System for Training Novice Cobot Programmers

Bilge Mutlu, Nathan Thomas White, Dakota Sullivan, Andrew J. Schoen
2 links
ACM Transactions on Human-Robot Interaction
Teaching and Learning Programming
article

CoFrame: A System for Training Novice Cobot Programmers

Bilge Mutlu, Nathan Thomas White, Dakota Sullivan, Andrew J. Schoen
article en

Abstract

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications in which they are currently deployed, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and processes they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system and evaluated how automated expert feedback supports novice troubleshooting and user perception. We evaluated our system with novice and expert users to understand their experience and expectations for robot programming.

ACM Transactions on Human-Robot Interaction
University of Wisconsin–Madison (US)
Quality education
Openalex Percentile: Top 7%
Teaching and Learning Programming
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