跳到主要内容
AI News HubLIVE
来源内容 · 翻译待补全2 分钟阅读

待翻译:Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models

文章摘要

AI 服务暂时不可用,以下为来源摘要,待恢复后补全翻译:arXiv:2610.02398v1 Announce Type: new Abstract: A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happen. We condition on the first. A demonstration is compiled into an effect program: the 3D keypoint trajectories of the objects that moved, two points marking where each was held, and the configuration the scene ends in, with the demonstrator removed. PEWAM, a 71.5M-parameter world-action model, generates effect, robot execution, action and terminal state as four streams with independent flow-matching times, so clamping a program and sampling the execution turns inference into programming, re-solved closed loop from the live scene. On held-out LIBERO-Goal tasks, one demonstration's progr…

来源arXiv Robotics作者: Junyi Hu, Zhewen He, Zhenhua Li, Yi Fang
待翻译:Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models
报告错误

纠错通道尚未开通,可先复制下方文章信息留存。

查看更正说明
直接读正文

AI 服务暂时不可用,以下为来源正文,待恢复后补全翻译。

[Submitted on 1 Oct 2026] Title:Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models View a PDF of the paper titled Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models, by Junyi Hu and 3 other authors View PDF HTML (experimental) Abstract:A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happen. We condition on the first. A demonstration is compiled into an effect program: the 3D keypoint trajectories of the objects that moved, two points marking where each was held, and the configuration the scene ends in, with the demonstrator removed. PEWAM, a 71.5M-parameter world-action model, generates effect, robot execution, action and terminal state as four streams with independent flow-matching times, so clamping a program and sampling the execution turns inference into programming, re-solved closed loop from the live scene. On held-out LIBERO-Goal tasks, one demonstration's program completes 40 of 90 episodes, where the same backbone given a goal image or language, and published demonstration-conditioned methods, complete at most 19; on three of Meta-World's held-out classes it exceeds the best published results, though not on the five-class mean. Because a program is a set of coordinates, a person can edit it: the placement follows a shifted terminal state and the grasp turns with rotated contact points. The same program runs on four robot arms without retraining, and after a push, re-solving completes 23 of 60 episodes where replaying the demonstration completes 6. On a Franka arm, fine-tuned on real demonstrations of other tasks, programs compiled from single human videos complete 36 of 40 trials, against 22 for the same backbone conditioned on the video's last frame as a goal image. Subjects: Robotics (cs.RO) Cite as: arXiv:2610.02398 [cs.RO] (or arXiv:2610.02398v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.02398 arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhewen He [view email] [v1] Thu, 1 Oct 2026 19:25:04 UTC (20,677 KB) Full-text links: Access Paper: View a PDF of the paper titled Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models, by Junyi Hu and 3 other authors View PDF HTML (experimental) TeX Source view license 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?)

展开要点与分析

文章情报

工程师进阶

要点

  • AI 服务暂时不可用,系统已先保留来源内容与降级元数据。
  • arXiv:2610.02398v1 Announce Type: new Abstract: A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happe…

要点与分析由自动化流程生成,可能有误,请结合原始来源核实。