Chain-of-Thought (CoT) prompting improves reasoning in large language models (LLMs), but its reliance on unstructured text limits interpretability and executability in embodied tasks. Prior work has explored structured CoTs using scene or logic graphs, yet these remain fundamentally limited: they model only low-order relations, lack constructs like inheritance or behavioral abstraction, and provide no standardized semantics for sequential or conditional planning. We propose UML-CoT, a structured reasoning and planning framework that leverages Unified Modeling Language (UML) to generate symbolic CoTs and executable action plans. UML class diagrams capture compositional object semantics, while activity diagrams model procedural control flow. Our three-stage training pipeline combines supervised fine-tuning with Group Relative Policy Optimization (GRPO), including reward learning from answer-only data. We evaluate UML-CoT on MRoom-30k, a new benchmark of cluttered room-cleaning scenarios. UML-CoT outperforms unstructured CoTs in interpretability, planning coherence, and execution success, highlighting UML as a more expressive and actionable structured reasoning formalism.

翻译：思维链（CoT）提示改进了大语言模型（LLM）的推理能力，但其对非结构化文本的依赖限制了其在具身任务中的可解释性与可执行性。先前的工作探索了使用场景图或逻辑图的结构化CoT，但这些方法仍存在根本性局限：它们仅建模低阶关系，缺乏继承或行为抽象等构造，并且未为顺序或条件规划提供标准化语义。我们提出UML-CoT，一个利用统一建模语言（UML）生成符号化思维链和可执行行动计划的结构化推理与规划框架。UML类图捕捉组合性对象语义，而活动图则对过程控制流进行建模。我们的三阶段训练流水线结合了监督微调与组相对策略优化（GRPO），包括从仅答案数据中学习奖励。我们在MRoom-30k（一个新的杂乱房间清洁场景基准）上评估UML-CoT。UML-CoT在可解释性、规划连贯性和执行成功率方面均优于非结构化CoT，突显了UML作为一种更具表达力和可操作性的结构化推理形式。