FLD: Fourier Latent Dynamics for Structured Motion Representation and
  Learning
Chenhao Li Elijah Stanger-Jones Steve Heim Sangbae Kim
Motion trajectories offer reliable references for physics-based motion
learning but suffer from sparsity, particularly in regions that lack sufficient
data coverage. To address this challenge, we introduce a self-supervised,
structured representation and generation method that extracts spatial-temporal
relationships in periodic or quasi-periodic motions. The motion dynamics in a
continuously parameterized latent space enable our method to enhance the
interpolation and generalization capabilities of motion learning algorithms.
The motion learning controller, informed by the motion parameterization,
operates online tracking of a wide range of motions, including targets unseen
during training. With a fallback mechanism, the controller dynamically adapts
its tracking strategy and automatically resorts to safe action execution when a
potentially risky target is proposed. By leveraging the identified
spatial-temporal structure, our work opens new possibilities for future
advancements in general motion representation and learning algorithms.