解耦视觉、语言与动作：高效多任务机器人策略 DEM

Computer Science > Robotics arXiv:2609.18374 (cs) [Submitted on 16 Sep 2026] Title:Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies Authors:Xiatao Sun, Chen Liang, Ziyao Zeng, Qian Wang, Haoyang Zhang, Yue Sun, Qiucheng Li, Daniel Rakita View a PDF of the paper titled Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies, by Xiatao Sun and 7 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models attach an action module to a Vision-Language Model (VLM) with billions of parameters and pay for that backbone at every control step. For a low-level manipulation policy, this cost may be unnecessary: the VLM supplies vision and language embeddings, and recent standalone vision encoders and encoder-only language models now match or exceed large VLMs on visual embedding and language understanding benchmarks. We study this question with a controlled experiment. Holding the demonstrations, the training budget, the tasks, and the measurement platform fixed, we vary the vision encoder, the language encoder, and the action head of a decoupled policy and compare against seven VLA baselines. The study yields the Decoupled Embodiment Model (DEM), which pairs a fine-tuned DINOv3 encoder and a frozen NeoBERT encoder with a MeanFlow head that generates each action chunk in a single forward pass. On 18 simulated manipulation tasks with held-out language paraphrases and randomized scenes, and on three real-robot tasks, DEM achieves observed success comparable to state-of-the-art VLM-backbone policies under our evaluation protocol, while running at eight to seventeen times their inference frequency and drawing six to fifteen times less energy per inference. Within this task scope, modern decoupled components offer a better success--latency--energy trade-off. Subjects: Robotics (cs.RO) Cite as: arXiv:2609.18374 [cs.RO] (or arXiv:2609.18374v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.18374 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xiatao Sun [view email] [v1] Wed, 16 Sep 2026 09:28:34 UTC (2,566 KB) Full-text links: Access Paper: View a PDF of the paper titled Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies, by Xiatao Sun and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google 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?)