動作記憶による軌道最適化のウォームスタート
Memory of Motion for Warm-starting Trajectory Optimization
ロボットの経路データベースから関数近似と次元削減でタスクと経路の対応を学習し、軌道最適化の良質な初期解を提供する手法を提案。PR2とAtlasで有効性を示した。
著者: Teguh Santoso Lembono, Antonio Paolillo, Emmanuel Pignat, Sylvain Calinon
分類: cs.RO
原文アブストラクト
Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. Three function approximators are compared: $k$-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the memory can be used as a metric to choose between several possible goals, and using an ensemble method to combine different function approximators results in a significantly improved warm-starting performance. We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas.
