AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 14 Sep 2026] Title:ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts View a PDF of the paper titled ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts, by Peiqi Yu and 5 other authors View PDF HTML (experimental) Abstract:We present ManiSkillFormer, a neuro-symbolic framework for demonstration-free and compositional robotic manipulation. Instead of learning end-to-end visuomotor policies, ManiSkillFormer introduces task-conditioned geometric contracts that explicitly structure the interface between perception and action. Each manipulation skill declares the semantic geometric primitives required for execution, such as object keypoints and surface normals. Building on human-defined skill structures, LLM agents generate these contracts and corresponding motion templates for different objects and task contexts. These contracts guide the perception module to ground task-relevant 3D primitives from observations, which are then used to instantiate reusable motion templates stored in a skill library. We evaluate ManiSkillFormer on Galaxea R1-Lite dual-arm robot across three settings: zero-shot pick-and-place over 8 object categories with 30 different instances, functional manipulation tasks including unscrewing, pouring, pressing, and folding, and 3 long-horizon tasks. ManiSkillFormer achieves higher average success rates than the evaluated baselines and two ablated pipelines: 88.24% for demonstration-free pick-and-place, 75.00% average success on functional manipulation and 50--80% completion rates across the long-horizon tasks. These results show that our design enables composable and reusable manipulation across objects and tasks without per-object policy fine-tuning or additional robot demonstrations. Subjects: Robotics (cs.RO) Cite as: arXiv:2609.16331 [cs.RO] (or arXiv:2609.16331v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.16331 arXiv-issued DOI via DataCite (pending registration) Submission history From: Peiqi Yu [view email] [v1] Mon, 14 Sep 2026 20:45:53 UTC (8,690 KB) Full-text links: Access Paper: View a PDF of the paper titled ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts, by Peiqi Yu and 5 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 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?)