Linear Temporal Logic Translation via Human-Inspired Self-Constrained Reasoning for Robot Task Specification

2026-08-28

AI Summary

本文提出自我约束推理（SCR）框架，通过将结构知识内化到模型决策过程中，有效解决将人类指令翻译成线性时序逻辑的问题。

Abstract

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.

Problem

• Linear Temporal Logic
• human instructions
• task specification
• constraint satisfaction
• robotic execution

Innovation

• Self-Constrained Reasoning
• Linear Temporal Logic
• Hierarchical Decision-Making
• Structural Knowledge Internalization