Abstract
In this talk, we address the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates human motion prediction with Task and Motion planning (TAMP) to address human-robot collaboration tasks. We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we extend a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP), which replans periodically to handle the mismatch between the human motion prediction and the actual human behavior.
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