跳到主要內容
AI News HubLIVE
來源內容 · 翻譯待補全2 分鐘閱讀

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

文章摘要

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2610.06926v1 Announce Type: new 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 ma…

來源arXiv Robotics作者: Mousumi Das, Aditeya Prajapati, Abrar Anwar, Jesse Thomason
待翻譯:SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
報告錯誤

更正渠道尚未開通,可先複製下方文章資訊留存。

查看更正說明
直接讀正文

AI 服務暫時不可用,以下為來源正文,待恢復後補全翻譯。

[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 View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models, by Mousumi Das and 3 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

展開要點與分析

文章情報

工程師進階

要點

  • AI 服務暫時不可用,系統已先保留來源內容與降級元數據。
  • arXiv:2610.06926v1 Announce Type: new Abstract: Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. Howeve…

要點與分析由自動化流程生成,可能有誤,請結合原始來源核實。