クープマン作用素と動的運動プリミティブを統合した深層学習による模倣学習
Deep Learning for Koopman-based Dynamic Movement Primitives
クープマン作用素と動的運動プリミティブを組み合わせ、非線形な運動を線形潜在空間に射影することで、少数のデモンストレーションから複雑な動作を学習する手法を提案した。
著者: Tyler Han, Carl Glen Henshaw
分類: cs.RO, cs.LG
原文アブストラクト
The challenge of teaching robots to perform dexterous manipulation, dynamic locomotion, or whole--body manipulation from a small number of demonstrations is an important research field that has attracted interest from across the robotics community. In this work, we propose a novel approach by joining the theories of Koopman Operators and Dynamic Movement Primitives to Learning from Demonstration. Our approach, named \gls{admd}, projects nonlinear dynamical systems into linear latent spaces such that a solution reproduces the desired complex motion. Use of an autoencoder in our approach enables generalizability and scalability, while the constraint to a linear system attains interpretability. Our results are comparable to the Extended Dynamic Mode Decomposition on the LASA Handwriting dataset but with training on only a small fractions of the letters.
