VIGOR: Zero-Shot Visual Generalization via Latent-Space Consistency in Model-Based Reinforcement Learning

The paper introduces VIGOR, a framework for zero‑shot visual generalization in model‑based reinforcement learning. VIGOR enforces latent‑space consistency through asymmetric weak‑to‑strong augmentations, dynamics‑level consistency, and encoder‑level stabilization, allowing the agent to handle unseen visual distractions while maintaining sample efficiency. Experiments on the DeepMind Control Suite and Robosuite demonstrate that VIGOR outperforms state‑of‑the‑art baselines, achieving significant gains in both environments.

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• reinforcement-learning
• benchmarks