Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration

Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those mode...

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Bibliographic Details
Published in:2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 743 - 751
Main Authors: Tung, Yi-Shiuan, Luebbers, Matthew B., Roncone, Alessandro, Hayes, Bradley
Format: Conference Proceeding
Language:English
Published: ACM 11.03.2024
Subjects:
Accuracy
Adaptation models
augmented reality
Collaboration
environment adaptation
human-robot collaboration
legibility
motion prediction
Prediction algorithms
Predictive models
Robot kinematics
Uncertainty
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Summary:Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.CCS CONCEPTS* Computing methodologies → Robotic planning; Planning under uncertainty; * Human-centered computing → Mixed / augmented reality.
DOI:10.1145/3610977.3635003