  Predicting human motion in unstructured and dynamic environments is difficult
as humans naturally exhibit complex behaviors that can change drastically from
one environment to the next. In order to alleviate this issue, we propose to
encode the lower level aspects of human motion separately from the higher level
geometrical aspects, which we believe will generalize better over environments.
In contrast to our prior work~\cite{kratzer2018}, we encode the short-term
behavior by using a state-of-the-art recurrent neural network structure instead
of a Gaussian process. In order to perform longer-term behavior predictions
that account for variation in tasks and environments, we propose to make use of
gradient-based trajectory optimization. Preliminary experiments on real motion
data demonstrate the efficacy of the approach.
