  Motion prediction in unstructured environments is a difficult problem and is
essential for safe and efficient human-robot space sharing and collaboration.
In this work, we focus on manipulation movements in environments such as homes,
workplaces or restaurants, where the overall task and environment can be
leveraged to produce accurate motion prediction. For these cases we propose an
algorithmic framework that accounts explicitly for the environment geometry
based on a model of affordances and a model of short-term human dynamics both
trained on motion capture data. We propose dedicated function networks for
graspability and placebility affordances and we make use of a dedicated RNN for
short-term motion prediction. The prediction of grasp and placement probability
densities are used by a constraint-based trajectory optimizer to produce a
full-body motion prediction over the entire horizon. We show by comparing to
ground truth data that we achieve similar performance for full-body motion
predictions as using oracle grasp and place locations.
