Abstract
An interaction between a robot and a human could be difficult with only reactive mechanisms, especially in a social interaction, because the robot usually needs time to plan its movement. This paper discusses a motion generation system for humanoid robots to perform interactions with human motion prediction. To learn a human motion, a Long Short-Term Memory is trained using a public dataset. The effectiveness of the proposed technique is demonstrated by performing a handshake with a humanoid robot. Instead of following the human palm, the robot learns to predict the hand-meeting point. By using three metrics namely the smoothness, timeliness, and efficiency of the robot movements, the experimental results of various motion plans are compared. The predictive method shows a balanced trade-off point in all the metrics.
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Vinayavekhin, P. et al. (2017). Human-Like Hand Reaching by Motion Prediction Using Long Short-Term Memory. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_16
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DOI: https://doi.org/10.1007/978-3-319-70022-9_16
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