World Models as Reference Trajectories for Rapid Motor Adaptation

• URL: http://arxiv.org/abs/2505.15589v1
• Date: Wed, 21 May 2025 14:46:41 GMT
• Title: World Models as Reference Trajectories for Rapid Motor Adaptation
• Authors: Carlos Stein Brito, Daniel McNamee,
• Abstract summary: Reflexive World Models (RWM) is a dual control framework that uses world model predictions as implicit reference trajectories for rapid adaptation.<n>Our method separates the control problem into long-term reward through reinforcement learning and robust motor execution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying learned control policies in real-world environments poses a fundamental challenge. When system dynamics change unexpectedly, performance degrades until models are retrained on new data. We introduce Reflexive World Models (RWM), a dual control framework that uses world model predictions as implicit reference trajectories for rapid adaptation. Our method separates the control problem into long-term reward maximization through reinforcement learning and robust motor execution through rapid latent control. This dual architecture achieves significantly faster adaptation with low online computational cost compared to model-based RL baselines, while maintaining near-optimal performance. The approach combines the benefits of flexible policy learning through reinforcement learning with rapid error correction capabilities, providing a principled approach to maintaining performance in high-dimensional continuous control tasks under varying dynamics.

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