让机器人理解人类指令，自动转成精确的执行规范
Linear Temporal Logic Translation via Human-Inspired Self-Constrained Reasoning for Robot Task Specification
- 用内置约束的推理框架，让模型在决策时自动遵守规则
- 在测试中约束满足率提升，对新指令也能准确适配
- 适合需要安全、可验证任务规划的机器人系统
许多机器人任务具有时间延展性，需精确指定子目标、约束及其时序关系。然而人类通常用自然语言描述任务，存在歧义、不完整和上下文依赖等问题。将人类指令转化为形式化任务规范（如线性时序逻辑LTL）对实现可验证且安全的机器人执行至关重要。现有基于大模型的翻译方法采用开放推理或事后约束强化，前者易违反领域约束，后者可能破坏对新指令的推理能力。本文提出自约束推理（SCR）框架，通过将结构化知识内嵌于模型决策过程，而非外部过滤，平衡了约束遵守与泛化能力。结合结构约束表示与分层决策机制，使推理在形式化空间内进行，同时保持对未见指令的适应性。实验表明，SCR在领域约束满足率和泛化性能上均有提升，提供了一种高效且可解释的人类意图到可验证规范的转换方法。
原文摘要 · Abstract (English)
Many robotic tasks are temporally extended and demand precise specifications of subgoals, constraints, and their temporal ordering. Yet human operators typically communicate such tasks in natural language, which is inherently ambiguous, underspecified, and context dependent. Translating human instructions into formal task specifications, such as Linear Temporal Logic (LTL), is therefore essential for verifiable and safe robotic execution. Existing LLM-based translators attempt to bridge this gap through open-ended reasoning or post-hoc constraint enforcement, but the former may violate domain constraints, whereas the latter can disrupt the reasoning needed for novel instructions. This paper proposes Self-Constrained Reasoning (SCR), a framework that mitigates this trade-off by internalizing structural knowledge into the model's decision-making process rather than imposing it as an external filter. By combining a structural constraint representation with a hierarchical decision-making formulation, SCR guides reasoning within a formally grounded space while preserving adaptability to unseen instructions. Experiments show that SCR improves both domain-constraint satisfaction and generalization, providing an effective and interpretable approach for translating human intent into verifiable specifications for robotic execution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。