[Submitted on 3 Oct 2026]
Title:SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
View a PDF of the paper titled SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models, by Mousumi Das and 3 other authors
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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%.
Comments: 9 pages , 4 figures,Under review for ICRA 2027
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.06926 [cs.RO]
(or arXiv:2610.06926v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.06926
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aditeya Prajapati [view email] [v1] Sat, 3 Oct 2026 00:12:20 UTC (1,355 KB)
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