SOURCE-LINKED INTELLIGENCE
BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models
Vision-language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale errors undo all prior progress. Online reinforcement learning (RL) can optimize exactly these actions, but free exploration is far too costly on real robots, which makes human corrections indispensable. However, existing online RL methods for VLAs either cannot incorporate such corrections or fold them into undifferentiated supervision. Yet human corrections are not uniformly noisy but reliable along some action dimensions and variable along others.
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T07:14:20.000Z
First collected: 2026-09-24T01:22:21.678Z. This is not the publication date.