リー群上の安定ベクトル場の学習
Learning Stable Vector Fields on Lie Groups
ロボットの姿勢全体に対する安定・滑らか・反応的なベクトル場を、ユークリッド空間ではなくリー群SE(2)/SE(3)上で学習する手法を提案し、シミュレーションと実機で有効性を示した。
著者: Julen Urain, Davide Tateo, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the performance of our proposed vector field model to learn stable vector fields for full robot poses as SE(2) and SE(3) in both simulated and real robotics tasks.
