提出可验证的双手操作标注结构，提升标注一致性与可读性。
SSC: A Verifiable Structured Representation for Bimanual Manipulation Labelling
- 用状态转移结构表示任务链，包含动作组件与场景图
- 在BEHAVIOR-1K上验证13个视觉语言模型，发现标注异常
- 支持自然语言生成、逻辑校验与缺失信息补全
子任务标签将长时序操作示范分解为短语义片段，用于策略训练与评估。自然语言描述易读，但语言差异使自动验证困难；固定模板格式（如BEHAVIOR-1K的skill_annotation）过于细分，影响可读性与标注一致性。本文提出结构化子任务链（SSC），一种介于两者之间的状态转移表示形式。一个示范由一系列结构化子任务模板（SST）构成，每个SST包含核心动作成分（主体、谓词、宾语）、灵活条件（如空间或工具性短语）、独立于手臂动作的基础运动字段，以及后状态场景图。基于此格式，SSC支持三项视觉语言辅助功能：将SST渲染为自然语言、依据四条状态转移规则校验链式结构、通过查询解析级联完成未指定字段。我们在BEHAVIOR-1K数据集（50个任务，每任务3个示范，共2,357个标注动作单元）上实现该流程，评估13个主流视觉语言模型作为候选验证器，并报告标注异常。
原文摘要 · Abstract (English)
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.
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