リカレントニューラルネットワーク力学モデルと軌道最適化による動作予測
Motion Prediction with Recurrent Neural Network Dynamical Models and Trajectory Optimization
人間の動作を低次の運動要素と高次の幾何要素に分けて表現し、RNNで短期予測、勾配ベースの軌道最適化で長期予測を行う手法を提案した。実データで有効性を確認。
著者: Philipp Kratzer, Marc Toussaint, Jim Mainprice
分類: cs.RO
原文アブストラクト
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.
