リーマン多様体上のガウス分布：ロボット学習と適応制御への応用
Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control
リーマン幾何学と確率表現を組み合わせ、多様体上のガウス分布を用いたクラスタリング・回帰・制御などのロボット学習手法を概説し、義手制御や水中ロボット遠隔操作の応用例を紹介したレビュー論文。
著者: Sylvain Calinon
分類: cs.RO, cs.LG
原文アブストラクト
This article presents an overview of robot learning and adaptive control applications that can benefit from a joint use of Riemannian geometry and probabilistic representations. The roles of Riemannian manifolds, geodesics and parallel transport in robotics are first discussed. Several forms of manifolds already employed in robotics are then presented, by also listing manifolds that have been underexploited but that have potentials in future robot learning applications. A varied range of techniques employing Gaussian distributions on Riemannian manifolds is then introduced, including clustering, regression, information fusion, planning and control problems. Two examples of applications are presented, involving the control of a prosthetic hand from surface electromyography (sEMG) data, and the teleoperation of a bimanual underwater robot. Further perspectives are finally discussed, with suggestions of promising research directions.
