A Study Of Reaching Motions For Collaborative Human-Robot Interaction

PROCEEDINGS OF THE 2018 INTERNATIONAL SYMPOSIUM ON EXPERIMENTAL ROBOTICS(2020)

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Abstract
In human-human interactions, individuals naturally achieve fluency by anticipating the partner's actions. This predictive ability is largely lacking in collaborative robots, leading to inefficient human-robot interactions. Fluent meshing in human-robot collaboration requires the robot to make its intentions clear to its human collaborator. We propose a unified generative model of human reaching motions that allows the robot to (a) infer human intent, and then (b) plan its motion to be legible, or intent-expressive. We conducted a study on human reaching motion and constructed an elliptical motion model that is shown to yield a good fit to empirical data. In future studies, we plan to confirm the effectiveness of this model in predicting human intent and conveying robot intent for achieving fluency in human-robot handovers.
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