  In this paper, we tackle the problem of human-robot coordination in sequences
of manipulation tasks. Our approach integrates hierarchical human motion
prediction with Task and Motion Planning (TAMP). 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 propose a dynamic version of the TAMP algorithm Logic-Geometric
Programming (LGP). Our version of Dynamic LGP, replans periodically to handle
the mismatch between the human motion prediction and the actual human behavior.
We assess the efficacy of the approach by training the prediction algorithms
and testing the framework on the publicly available MoGaze dataset.
