Adapting Vision-Language-Action Models to Unknown Visual Disruptions During Execution
cs.RO, cs.AI
Submitted: 2026-10-06
Updated: 2026-10-06
Sources
- EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Colosseum V2: Benchmarking Generalization for Vision-Language-Action Models
- Improving Robustness of Vision-Language-Action Models by Restoring Corrupted Visual Inputs
- Test-Time Training for Visual Foresight Vision-Language-Action Models
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison
- STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbations
- SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
- TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
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