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

2024-12-10

arXiv.org

AI Summary

Imitation learning exhibits poor generalization to out-of-support (OOS) regions, and existing stable dynamical system approaches only guarantee asymptotic convergence while neglecting transient robustness. Method: We propose a novel policy framework grounded in contraction dynamics, the first to intrinsically embed contraction into the policy architecture—ensuring parameter-independent global convergence. Our approach integrates recurrent equilibrium networks (RENs), invertible coupling layers, and unconstrained optimization, yielding theoretically derived upper bounds on worst-case and expected loss. Contribution/Results: These bounds rigorously guarantee deployment robustness under distributional shift. In simulated robotic manipulation and navigation tasks, the framework significantly improves OOS recovery efficiency and convergence speed, consistently outperforming state-of-the-art stable system methods across all evaluated metrics.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 using 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. Furthermore, we provide theoretical upper bounds for worst-case and expected loss terms, rigorously establishing the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements in robotics manipulation and navigation tasks in simulation.

Problem

• Ensures policy convergence despite perturbations
• Guarantees contractivity for any parameter choice
• Improves out-of-sample recovery in imitation learning

Innovation

• Contractive dynamical systems ensure policy convergence
• Recurrent equilibrium networks guarantee contractivity
• Theoretical bounds establish deployment reliability