Predicting human motion in unstructured and dynamic environments is challenging. Human
behavior arises from complex sensory-motor couplings processes that can change
drastically depending on environments or tasks. In order to alleviate this issue, we propose
to encode the lower level aspects of human motion separately from the higher level
geometrical aspects using data driven dynamical models. In order to perform longer term
behavior predictions that account for variation in tasks and environments, we propose to …