Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery

Amin Abyaneh, Mahrokh Ghoddousi Boroujeni, Hsiu-Chin Lin, Giancarlo Ferrari-Trecate

Keywords: Learning from demonstration, Safe imitation learning, Robotics, Dynamical system, Contraction theory

TL;DR: We introduce a class of contractive imitation policies with theoretical guarantees and out-of-sample error bounds for robot learning.

Abstract:

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 for our codebase and highlight of the results.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 5171