CoRI: Communication of Robot Intent for Physical Human-Robot Interaction
Junxiang Wang, Emek Bar{\i}\c{s} K\"u\c{c}\"uktabak, Rana Soltani Zarrin, Zackory Erickson

TL;DR
This paper presents CoRI, a pipeline that automatically generates natural language explanations of a robot's planned actions from visual and motion data, enhancing transparency in physical human-robot interactions.
Contribution
CoRI is a novel, automated method that translates robot motion plans and perception into natural language communication without task-specific tuning.
Findings
CoRI improves communication clarity in human-robot interaction tasks.
Participants better understood robot intentions with CoRI compared to baseline.
CoRI effectively conveys motion details and collaborative cues.
Abstract
Clear communication of robot intent fosters transparency and interpretability in physical human-robot interaction (pHRI), particularly during assistive tasks involving direct human-robot contact. We introduce CoRI, a pipeline that automatically generates natural language communication of a robot's upcoming actions directly from its motion plan and visual perception. Our pipeline first processes the robot's image view to identify human poses and key environmental features. It then encodes the planned 3D spatial trajectory (including velocity and force) onto this view, visually grounding the path and its dynamics. CoRI queries a vision-language model with this visual representation to interpret the planned action within the visual context before generating concise, user-directed statements, without relying on task-specific information. Results from a user study involving robot-assisted…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Social Robot Interaction and HRI · Hand Gesture Recognition Systems
