リーマン多様体上で安定なロボットスキルを学習する手法
Learning Stable Robotic Skills on Riemannian Manifolds
微分幾何学を活用し、単純な安定力学系を複雑なロボットスキルへ写像するデータ効率の良い学習法を提案。姿勢やインピーダンスの幾何制約を満たしつつ目標への収束を保証し、実機で検証した。
著者: Matteo Saveriano, Fares J. Abu-Dakka, Ville Kyrki
分類: cs.RO
原文アブストラクト
In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic transformation that maps simple stable dynamical systems onto complex robotic skills. By exploiting mathematical tools from differential geometry, the method ensures that the learned skills fulfill the geometric constraints imposed by the underlying manifolds, such as unit quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices for impedance, while preserving the convergence to a given target. The proposed approach is firstly tested in simulation on a public benchmark, obtained by projecting Cartesian data into UQ and SPD manifolds, and compared with existing approaches. Apart from evaluating the approach on a public benchmark, several experiments were performed on a real robot performing bottle stacking in different conditions and a drilling task in cooperation with a human operator. The evaluation shows promising results in terms of learning accuracy and task adaptation capabilities.
