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cs.RO2026
FIRE-VLA: Failure-Informed Self-Evolution for Vision-Language-Action Models in Autonomous Driving
Hao Dou
Reinforcement learning improves autonomous-driving vision-language-action (VLA) models by evaluating trajectories sampled from the current policy. Group relative policy optimizatio…
cs.RO2026
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning
Haisheng Su, Zongdai Liu, Xin Jin +13
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…