AI RESEARCH
PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models
arXiv CS.CV
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ArXi:2606.03598v1 Announce Type: cross Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation. However, deploying these models in open-ended environments requires continuously acquiring novel skills, a process that inevitably triggers severe catastrophic forgetting of previously learned behaviors. While experience replay (ER) serves as a standard mitigating strategy, naive uniform sampling fundamentally misaligns with the temporal characteristics of manipulation trajectories.