Control

Dynamic Movement Primitives (DMP)

Dynamic Movement Primitives (DMP) are a framework for encoding motions as stable nonlinear dynamical systems, developed by Ijspeert, Nakanishi, and Schaal in the early 2000s. A spring-damper attractor guarantees convergence to a goal while a learned forcing term, fit from a single demonstration, shapes the trajectory; the formulation supports temporal scaling, goal changes, and rhythmic as well as discrete motions. DMPs were a workhorse of pre-deep-learning imitation learning.

Why it matters for physical AI

DMPs remain attractive when guarantees matter: one demonstration suffices, goal convergence is provable, and trajectories adapt online, properties that black-box policy networks are still working to match in safety-conscious settings.