弾性マップを用いたロボットの見まね学習
Robot Learning from Demonstration Using Elastic Maps
人間の実演を弾性マップ（バネで結ばれたノードのグラフ）としてモデル化し、凸最適化でロボットスキルを学習・汎化する手法を提案。UR5eで実機検証した。
著者: Brendan Hertel, Matthew Pelland, S. Reza Ahmadzadeh
分類: cs.RO
原文アブストラクト
Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimization-based LfD method that encodes demonstrations as elastic maps. An elastic map is a graph of nodes connected through a mesh of springs. We build a skill model by fitting an elastic map to the set of demonstrations. The formulated optimization problem in our approach includes three objectives with natural and physical interpretations. The main term rewards the mean squared error in the Cartesian coordinate. The second term penalizes the non-equidistant distribution of points resulting in the optimum total length of the trajectory. The third term rewards smoothness while penalizing nonlinearity. These quadratic objectives form a convex problem that can be solved efficiently with local optimizers. We examine nine methods for constructing and weighting the elastic maps and study their performance in robotic tasks. We also evaluate the proposed method in several simulated and real-world experiments using a UR5e manipulator arm, and compare it to other LfD approaches to demonstrate its benefits and flexibility across a variety of metrics.
