階層的人間動作予測と論理幾何プログラミングによる干渉最小の人間-ロボット協調
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
逆強化学習とRNNを組み合わせた階層的人間動作予測と、動的に再計画する論理幾何プログラミング(TAMP)を統合し、操作タスク列における人間とロボットの干渉を最小化する協調手法を提案。MoGazeデータセットで有効性を検証した。
著者: An T. Le, Philipp Kratzer, Simon Hagenmayer, Marc Toussaint, Jim Mainprice
分類: cs.RO
原文アブストラクト
In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP). Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset.
