SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models

Abstract:Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do not perform well across different task states and environments. We introduce a framework for dynamically composing multiple VLA policies during execution: StepWise Action Policy Routing (SWAP). SWAP formulates policy routing as an offline reinforcement learning problem, learning a routing critic that selects the most appropriate policy at each decision step given the current observation. SWAP enables robots to select new policies to execute online rather than committing to a single policy for the duration of an episode. We evaluate SWAP on both real-world DROID manipulation tasks and LIBERO simulation experiments. SWAP improves over fixed-policy execution and routing baselines, giving absolute improvements in real-world task success up to 33% while reducing successful trajectory robot action step length by 28.3%.

Robotics (cs.RO)

arXiv:2610.06926 [cs.RO]

(or arXiv:2610.06926v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.06926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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