V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
📰 ArXiv cs.AI
Learn how V-VLAPS improves vision-language-action models with value-guided planning for robotic manipulation tasks, enhancing execution under distribution shift and long-horizon tasks
Action Steps
- Implement V-VLAPS using a pretrained vision-language-action model to guide tree search
- Configure the planner to use value-guided node selection instead of policy priors and visit-count exploration
- Test the V-VLAPS approach on a robotic manipulation task with distribution shift and long-horizon structure
- Compare the performance of V-VLAPS with existing VLA-guided planning methods
- Apply V-VLAPS to other domains with similar task structures to evaluate its generalizability
Who Needs to Know This
Researchers and engineers working on vision-language-action models for robotic manipulation can benefit from this approach to improve task execution and planning
Key Insight
💡 Value-guided planning can enhance the execution of vision-language-action models under distribution shift and long-horizon tasks
Share This
💡 Improve robotic manipulation tasks with V-VLAPS, a value-guided planning approach for vision-language-action models! #AI #Robotics
Key Takeaways
Learn how V-VLAPS improves vision-language-action models with value-guided planning for robotic manipulation tasks, enhancing execution under distribution shift and long-horizon tasks
Full Article
Title: V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
Abstract:
arXiv:2601.00969v2 Announce Type: replace-cross Abstract: 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 la
Abstract:
arXiv:2601.00969v2 Announce Type: replace-cross Abstract: 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 la
DeepCamp AI