AI RESEARCH
V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
arXiv CS.AI
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ArXi:2601.00969v2 Announce Type: replace-cross Vision-language-action (VLA) models provide strong action priors for robotic manipulation, but their reactive behavior can fail under distribution shift and long-horizon task structure. Recent VLA-guided planning methods improve execution by using pretrained policies to guide tree search, yet node selection still depends heavily on policy priors and visit-count exploration. Consequently, when the policy favors poor actions, the planner lacks a learned value signal to correct this bias.