SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
Overview
SWAP is a framework that dynamically composes multiple VLA policies during robot task execution, using offline reinforcement learning for policy routing.
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- arXiv Robotics — research abstractsSWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
This research proposes SWAP, a framework that dynamically composes multiple Vision-Language-Action (VLA) policies during robot task execution. By formulating policy routing as an offline reinforcement learning problem, SWAP enables robots to select new policies online, improving real-world task success by up to 33% and reducing successful trajectory action steps by 28.3%.
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