動作最適化と再帰型ニューラルネットワークによる人体全身運動の予測
Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
短期の身体内部ダイナミクスをRNNで、環境制約を勾配ベースの軌道最適化で扱うことで、未知の環境構造にも対応できる人体全身運動予測フレームワークを提案し、人間と協調するロボット軌道計画への応用も示した。
著者: Philipp Kratzer, Marc Toussaint, Jim Mainprice
分類: cs.RO, cs.CV
原文アブストラクト
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.
