  Human movement prediction 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 a prediction framework that decouples
short-term prediction, linked to internal body dynamics, and long-term
prediction, linked to the environment and task constraints. In this work we
investigate encoding short-term dynamics in a recurrent neural network, while
we account for environmental constraints, such as obstacle avoidance, using
gradient-based trajectory optimization. Experiments on real motion data
demonstrate that our framework improves the prediction with respect to
state-of-the-art motion prediction methods, as it accounts to beforehand unseen
environmental structures. Moreover we demonstrate on an example, how this
framework can be used to plan robot trajectories that are optimized to
coordinate with a human partner.
