Imitation Learning via Discrete and Rhythmic Primitive Composition

Long-horizon robotic manipulation is inherently composed of semantically meaningful sub-tasks. While Movement Primitives (MPs) can model these components, their composition typically relies on manual engineering or sample-inefficient reinforcement learning.

From the abstract. Our summary is in progress.