Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems

• URL: http://arxiv.org/abs/2604.19980v1
• Date: Tue, 21 Apr 2026 20:40:22 GMT
• Title: Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems
• Abstract summary: This paper presents a model-based reinforcement learning framework for optimal closed-loop control of nonlinear robotic systems.<n>The proposed approach learns linear lifted dynamics through Koopman operator theory and integrates the resulting model into an actor-critic architecture for policy optimization.<n>The framework is evaluated on several simulated nonlinear control benchmarks and two real-world hardware platforms.
• Score: 4.946483489399818
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed approach learns linear lifted dynamics through Koopman operator theory and integrates the resulting model into an actor-critic architecture for policy optimization, where the policy represents a parameterized closed-loop controller. To reduce computational cost and mitigate model rollout errors, policy gradients are estimated using one-step predictions of the learned dynamics rather than multi-step propagation. This leads to an online mini-batch policy gradient framework that enables policy improvement from streamed interaction data. The proposed framework is evaluated on several simulated nonlinear control benchmarks and two real-world hardware platforms, including a Kinova Gen3 robotic arm and a Unitree Go1 quadruped. Experimental results demonstrate improved sample efficiency over model-free RL baselines, superior control performance relative to model-based RL baselines, and control performance comparable to classical model-based methods that rely on exact system dynamics.

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