待翻譯：SSC: A Verifiable Structured Representation for Bimanual Manipulation Labelling
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.04425v1 Announce Type: new Abstract: Subtask labels decompose a long-horizon manipulation demonstration into shorter semantic segments for policy training and evaluation. Natural language descriptions are easy to read, but their linguistic variability makes automatic verification difficult. Rigid template formats, such as BEHAVIOR-1K's skill_annotation, are linguistically over-segmented, hindering both readability and annotation consistency. We propose the Structured Subtask Chain (SSC), a state-transition representation that bridges these extremes. A demonstration is a sequence of Structured Subtask Template (SST) entries. Each SST stores core action components (subject, predicate, object), flexible conditions (adverbial modifiers such as spatial or instrumental phrases), a base-motion field separate from arm actions, and an after-state scene graph. Built on this format, SSC supports three vision-language assisted functions: rendering SSTs as natural language, checking the assembled chain against four state-transition rules, and completing underspecified fields through a query resolution cascade. We instantiate the pipeline on BEHAVIOR-1K (50 tasks, 3 episodes per task, 2,357 annotated action cells) for logic verification and content completion, evaluating 13 selected state-of-the-art VL models as candidate verifiers and reporting labelling anomalies.
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 5 Aug 2026] Title:SSC: A Verifiable Structured Representation for Bimanual Manipulation Labelling View a PDF of the paper titled SSC: A Verifiable Structured Representation for Bimanual Manipulation Labelling, by Yupu Lu and 6 other authors View PDF HTML (experimental) Abstract:Subtask labels decompose a long-horizon manipulation demonstration into shorter semantic segments for policy training and evaluation. Natural language descriptions are easy to read, but their linguistic variability makes automatic verification difficult. Rigid template formats, such as BEHAVIOR-1K's skill_annotation, are linguistically over-segmented, hindering both readability and annotation consistency. We propose the Structured Subtask Chain (SSC), a state-transition representation that bridges these extremes. A demonstration is a sequence of Structured Subtask Template (SST) entries. Each SST stores core action components (subject, predicate, object), flexible conditions (adverbial modifiers such as spatial or instrumental phrases), a base-motion field separate from arm actions, and an after-state scene graph. Built on this format, SSC supports three vision-language assisted functions: rendering SSTs as natural language, checking the assembled chain against four state-transition rules, and completing underspecified fields through a query resolution cascade. We instantiate the pipeline on BEHAVIOR-1K (50 tasks, 3 episodes per task, 2,357 annotated action cells) for logic verification and content completion, evaluating 13 selected state-of-the-art VL models as candidate verifiers and reporting labelling anomalies. Comments: 8 main pages Subjects: Robotics (cs.RO) Cite as: arXiv:2608.04425 [cs.RO] (or arXiv:2608.04425v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.04425 arXiv-issued DOI via DataCite (pending registration) Submission history From: Yupu Lu [view email] [v1] Wed, 5 Aug 2026 04:09:16 UTC (928 KB) Full-text links: Access Paper: View a PDF of the paper titled SSC: A Verifiable Structured Representation for Bimanual Manipulation Labelling, by Yupu Lu and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-08 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?)