Public articles linked to the same research event.
arXiv The authors propose VIGOR, a framework combining asymmetric weak-to-strong augmentation, dynamics-level consistency via direct latent regression that enforces augmentation-invariant transition predictions, and encoder-level stabilization, achieving zero-shot generalization to unseen visual distractions while retaining the sample efficiency of its model-based RL backbone; it outperforms state-of-the-art model-free and model-based baselines on the DeepMind Control Suite and Robosuite, surpassing the second-best baseline by 3.4% and 43.6% respectively, and ablations show that replacing the default augmentation with alternatives from distinct perturbation families preserves strong generalization, indicating that latent-space consistency rather than the augmentation choice drives robustness.
The authors propose VIGOR, a framework combining asymmetric weak-to-strong augmentation, dynamics-level consistency via direct latent regression that enforces augmentation-invariant transition predictions, and encoder-level stabilization, achieving zero-shot generalization to unseen visual distractions while retaining the sample efficiency of its model-based RL backbone; it outperforms state-of-the-art model-free and model-based baselines on the DeepMind Control Suite and Robosuite, surpassing the second-best baseline by 3.4% and 43.6% respectively, and ablations show that replacing the default augmentation with alternatives from distinct perturbation families preserves strong generalization, indicating that latent-space consistency rather than the augmentation choice drives robustness.
The authors propose VIGOR, a framework combining asymmetric weak-to-strong augmentation, dynamics-level consistency via direct latent regression that enforces augmentation-invariant transition predictions, and encoder-level stabilization, achieving zero-shot generalization to unseen visual distractions while retaining the sample efficiency of its model-based RL backbone; it outperforms state-of-the-art model-free and model-based baselines on the DeepMind Control Suite and Robosuite, surpassing the second-best baseline by 3.4% and 43.6% respectively, and ablations show that replacing the default augmentation with alternatives from distinct perturbation families preserves strong generalization, indicating that latent-space consistency rather than the augmentation choice drives robustness.
The authors propose VIGOR, a framework combining asymmetric weak-to-strong augmentation, dynamics-level consistency via direct latent regression that enforces augmentation-invariant transition predictions, and encoder-level stabilization, achieving zero-shot generalization to unseen visual distractions while retaining the sample efficiency of its model-based RL backbone; it outperforms state-of-the-art model-free and model-based baselines on the DeepMind Control Suite and Robosuite, surpassing the second-best baseline by 3.4% and 43.6% respectively, and ablations show that replacing the default augmentation with alternatives from distinct perturbation families preserves strong generalization, indicating that latent-space consistency rather than the augmentation choice drives robustness.