安定なニューラルODEを用いた深層力学系の学習
Learning Deep Dynamical Systems using Stable Neural ODEs
複数のアトラクタを持つ安定な潜在力学系をニューラルODEで学習し、状態微分情報や状態計測を必要とせずに軌道生成を実現する手法を提案。
著者: Andreas Sochopoulos, Michael Gienger, Sethu Vijayakumar
分類: cs.RO
原文アブストラクト
Learning complex trajectories from demonstrations in robotic tasks has been effectively addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning methods ensure stability of the generated trajectories; however, they have three shortcomings: a) the DS is assumed to have a single attractor, which limits the diversity of tasks it can achieve, b) state derivative information is assumed to be available in the learning process and c) the state of the DS is assumed to be measurable at inference time. We propose a class of provably stable latent DS with possibly multiple attractors, that inherit the training methods of Neural Ordinary Differential Equations, thus, dropping the dependency on state derivative information. A diffeomorphic mapping for the output and a loss that captures time-invariant trajectory similarity are proposed. We validate the efficacy of our approach through experiments conducted on a public dataset of handwritten shapes and within a simulated object manipulation task.
