Deep Learning for Koopman-based Dynamic Movement Primitives

Tyler Han; Carl Glen Henshaw

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arXiv · 2312.03328

Deep Learning for Koopman-based Dynamic Movement Primitives

Tyler Han·Carl Glen Henshaw

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Abstract

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.

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Tyler Han, Carl Glen Henshaw. 2023-12-06. Deep Learning for Koopman-based Dynamic Movement Primitives. https://arxiv.org/abs/2312.03328

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