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A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation
"... Abstract This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a func-tion of the current action of the human. The trajectory generation is based on actio ..."
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Abstract This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a func-tion of the current action of the human. The trajectory generation is based on action recognition and prediction of the human movement given intermit-tent observations of his/her positions under unknown speeds of execution; a problem typically found when using motion capture systems in scenarios that lead to occlusion. Of particular interest, the ability to predict the hu-man movement while observing the initial part of his/her trajectory allows for faster robot reactions, and as it will be shown, also eliminates the need of time-alignment of the training data. The method models the coupling be-tween human-robot movement primitives and is scalable in relation to the number of tasks. We evaluated the method using a 7-DoF lightweight robot arm equipped with a 5-finger hand in a multi-task collaborative assembly experiment, also comparing results with our previous method based on time-aligned trajectories. 1