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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Sheikholeslami, S., Lee, G., Hart, J.W., Srinivasa, S., Croft, E.A. (2020). A Study of Reaching Motions for Collaborative Human-Robot Interaction. In: Xiao, J., Kröger, T., Khatib, O. (eds) Proceedings of the 2018 International Symposium on Experimental Robotics. ISER 2018. Springer Proceedings in Advanced Robotics, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-33950-0_50
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