Towards Combining Motion Optimization and Data Driven Dynamical Models for Human Motion Prediction

Abstract

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 make use of gradient based constraint motion optimization. The present method is the first to our knowledge to combine motion optimization and data driven dynamical models for human motion prediction. We present results on synthetic and motion capture data of upper body reaching movements (see Figure 1) that demonstrate the efficacy of the approach with respect to simple baselines often mentioned in prior work.