Visual GeneralizationExpert
VIGOR: Zero-Shot Visual Generalization via Latent-Space Consistency in Model-Based Reinforcement Learning
This preprint introduces VIGOR, a model-based reinforcement learning framework that adds latent-space consistency to TD-MPC2 for zero-shot generalization to unseen visual distractions. On six DeepMind Control Suite tasks and four Robosuite tasks, it improves mean zero-shot returns by 3.4% over the second-best baseline on DMC and by 43.6% on Robosuite while matching TD-MPC2's sample efficiency. The work matters because it addresses a two-level compounding failure unique to recursive latent dynamics, which is critical for deploying sample-efficient MBRL in visually varying environments.