AFCE把未來物理效果當成預測目標

Computer Science > Robotics arXiv:2610.

13212 (cs) [Submitted on 9 Oct 2026 (v1), last revised 12 Oct 2026 (this version, v2)] Title:Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models Authors:Min Lin, Bokai Zhou, Jianqi Lin, Kaiyuan Yang, Yicheng Chang, Junjie Yu, Xiaofan Liu, Jianhua Han, Rui Li, Shen Zhao, Xiaodan Liang View a PDF of the paper titled Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models, by Min Lin and 10 other authors View PDF HTML (experimental) Abstract:Actions specify what a robot should execute, but do not describe what those commands will achieve.

Vision-language-action (VLA) policies nevertheless learn primarily by predicting action chunks from images, language, and state.We introduce Action--Future Coupled Effect Learning (AFCE), which makes a future-grounded physical Effect the policy's primary prediction target.

An offline tokenizer constructs temporally ordered Effect tokens from demonstrated actions, realized robot responses, and sparse visual transitions.Reconstruction objectives encourage the representation to retain executable detail and observed physical change.

A flow-matching VLA predicts Effects from current images, instruction, and robot state, while a state-conditioned decoder converts them into action chunks.Embodiment-specific interfaces allow single- and dual-arm tasks to share the Effect model.On DexJoCo, AFCE achieves $50.

97\%$ overall success, improving over $\pi_{0.5}$ and UVT by 11.82 and 15.21 percentage points, respectively.It leads on all five bimanual tasks, highlighting the value of Effect supervision for coordinated dexterous manipulation.The project page is available at this https URL.

Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.13212 [cs.RO] (or arXiv:2610.13212v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.

13212 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Min Lin [view email] [v1] Fri, 9 Oct 2026 08:29:46 UTC (5,434 KB) [v2] Mon, 12 Oct 2026 06:58:05 UTC (5,434 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models, by Min Lin and 10 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

RO < prev | next > new | recent | 2026-10 Change to browse by: cs cs.AI References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...

Data provided by: Bookmark 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?)