半適応型ニューラルネットワークによる人間の動作予測
Human Motion Prediction using Semi-adaptable Neural Networks
オフライン学習したニューラルネットにオンラインのパラメータ適応と不確かさ推定を組み合わせ、人間の動作をリアルタイムに予測する手法を提案した。
著者: Yujiao Cheng, Weiye Zhao, Changliu Liu, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
Human motion prediction is an important component to facilitate human robot interaction. Robots need to accurately predict human's future movement in order to safely plan its own motion trajectories and efficiently collaborate with humans. Many recent approaches predict human's movement using deep learning methods, such as recurrent neural networks. However, existing methods lack the ability to adapt to time-varying human behaviors, and many of them do not quantify uncertainties in the prediction. This paper proposes an approach that uses a semi-adaptable neural network for human motion prediction, and provides uncertainty bounds of the predictions in real time. In particular, a neural network is trained offline to represent the human motion transition model, and then recursive least square parameter adaptation algorithm (RLS-PAA) is adopted for online parameter adaptation of the neural network and for uncertainty estimation. Experiments on several human motion datasets verify that the proposed method significantly outperforms the state-of-the-art approach in terms of prediction accuracy and computation efficiency.
