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 lacks a learned value signal to correct this bias. Prior work has shown that VLA representations encod
The paper addresses current limitations in Vision-Language-Action (VLA) models, which are central to advanced robotic manipulation, providing a timely improvement to an emerging technology.
Improving VLA models for robotic manipulation by integrating value-guided planning enhances robot autonomy and reliability, critical for broader adoption in various sectors.
Current reactive VLA models will become more robust and capable of handling complex, long-horizon tasks and distribution shifts, reducing failure rates in robotic applications.
- · Robotics companies
- · Automation industry
- · Logistics and manufacturing
- · AI research institutions
- · Companies relying on less sophisticated automation
- · Manual labor in repetitive tasks
Robots will perform more complex tasks with fewer errors, leading to increased efficiency.
The enhanced capabilities of robotic manipulation will accelerate the deployment of autonomous systems in new industries.
This technological advancement could contribute to a larger societal discussion on the role of advanced AI in labor and industry.
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Read at arXiv cs.AI