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Anticipative Interaction Primitives for Human-Robot Collaboration

Guilherme Maeda; Aayush Maloo; Marco Ewerton; Rudolf Lioutikov; Jan Peters
In: 2016 AAAI Fall Symposia. AAAI Conference on Artificial Intelligence (AAAI-2016), November 17-19, Arlington, Virginia, USA, AAAI Press, 2016.


This paper introduces our initial investigation on the problem of providing a semi-autonomous robot collaborator with anticipative capabilities to predict human actions. Anticipative robot behavior is a desired characteristic of robot collaborators that lead to fluid, proactive interactions. We are particularly interested in improving reactive methods that rely on human action recognition to activate the corresponding robot action. Action recognition invariably causes delay in the robot’s response, and the goal of our method is to eliminate this delay by predicting the next human action. Prediction is achieved by using a lookup table containing variations of assembly sequences, previously demonstrated by different users. The method uses the nearest neighbor sequence in the table that matches the actual sequence of human actions. At the movement level, our method uses a probabilistic representation of interaction primitives to generate robot trajectories. The method is demonstrated using a 7 degree-offreedom lightweight arm equipped with a 5-finger hand on an assembly task consisting of 17 steps.

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