PRISM: Precision Forcing for Generalist Vision-Language-Action Policies
Overview
PRISM addresses Flow-Time Starvation by aligning supervision allocation with precision demand, boosting task success in precise manipulation tasks to 86.7% in simulation and real-world experiments. Previously, success rates were 38.3% in similar tasks. The method enhances VLM condition representation for better action control without new sensors. The approach combines pretrained VLMs with action experts, improving reliability in submillimeter cable insertion and screw tightening. The research is published on arXiv:2610.14704v1. Latest: PRISM improves mean end-to-end success from 38.3% to 86.7% in precise manipulation tasks like cable insertion and screw tightening, without new sensors. The method scores precision demand offline and strengthens supervision where errors are consequential. The research is published on arXiv:2610.14704v1.
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- arXiv Robotics — research abstractsPRISM: Precision Forcing for Generalist Vision-Language-Action Policies
This research introduces PRISM, a method that improves precision in vision-language-action (VLA) policies by addressing Flow-Time Starvation, a problem where training objectives fail to allocate sufficient supervision to critical moments. PRISM enhances task success rates in precise manipulation tasks like cable insertion and screw tightening without new sensors, achieving up to 86.7% end-to-end success in simulation and real-world experiments.
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