Deep Learning for Koopman-based Dynamic Movement Primitives
Tyler Han Carl Glen Henshaw
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.