Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
Amin Abyaneh Mahrokh Ghoddousi Boroujeni Hsiu-Chin Lin Giancarlo Ferrari-Trecate
Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies modeled by contractive dynamical systems, ensuring that all policy rollouts converge regardless of perturbations, and in turn, enable efficient OOS recovery. By leveraging recurrent equilibrium networks and coupling layers, the policy structure guarantees contractivity for any parameter choice, which facilitates unconstrained optimization. We also provide theoretical upper bounds for worst-case and expected loss to rigorously establish the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements for simulated robotic manipulation and navigation tasks. See [sites.google.com/view/contractive-dynamical-policies](https://sites.google.com/view/contractive-dynamical-policies) for our codebase and highlight of the results.
Learning from demonstration, Safe imitation learning, Robotics, Dynamical system, Contraction theory
We introduce a class of contractive imitation policies with theoretical guarantees and out-of-sample error bounds for robot learning.