ユークリッド化フロー：安定な力学系学習のための微分同相写像による簡約
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
人間の実演から複雑なロボット動作を、微分同相写像で単純な安定力学系に変換して学習する手法を提案し、ベンチマークと実機で有効性を示した。
著者: Muhammad Asif Rana, Anqi Li, Dieter Fox, Byron Boots, Fabio Ramos, Nathan Ratliff
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Robotic tasks often require motions with complex geometric structures. We present an approach to learn such motions from a limited number of human demonstrations by exploiting the regularity properties of human motions e.g. stability, smoothness, and boundedness. The complex motions are encoded as rollouts of a stable dynamical system, which, under a change of coordinates defined by a diffeomorphism, is equivalent to a simple, hand-specified dynamical system. As an immediate result of using diffeomorphisms, the stability property of the hand-specified dynamical system directly carry over to the learned dynamical system. Inspired by recent works in density estimation, we propose to represent the diffeomorphism as a composition of simple parameterized diffeomorphisms. Additional structure is imposed to provide guarantees on the smoothness of the generated motions. The efficacy of this approach is demonstrated through validation on an established benchmark as well demonstrations collected on a real-world robotic system.
