Motion Prediction with Recurrent Neural Network Dynamical Models and Trajectory Optimization
This work addresses motion prediction for robotics or human-computer interaction, but appears incremental as it builds on prior methods with modifications.
The paper tackles the problem of predicting human motion in dynamic environments by encoding lower-level motion aspects separately from higher-level geometry to improve generalization, and demonstrates preliminary efficacy on real motion data.
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.