用分层世界模型让机器人实时执行复杂时序指令，兼顾安全与进度。
hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance
- 分两级建模：高层预测任务进展，低层确保局部动作安全。
- 在CALVIN数据集上超越现有方法，成功完成含强约束的复杂指令。
- 适合需要高可靠性的机器人推理阶段控制，如工业操作场景。
机器人学习的核心目标之一是实现在运行时执行丰富的动态指令。尽管大规模语言条件策略已取得显著进展，但在处理时序结构和安全约束方面仍存在困难。线性时序逻辑（LTL）能有效表达复杂的非马尔可夫指令，但如何引导学习到的操纵策略满足LTL规范仍具挑战性，因现代策略生成短时域动作片段并闭环重规划，而绝大多数LTL规范需在长时域轨迹上评估。本文提出hint²，一种基于分层世界模型的推理时时间逻辑引导方法。核心思想是利用不同抽象层级的世界模型生成双重引导目标：高层模型预测动作引发的任务相关原子命题转移，以推进LTL自动机状态；低层动力学模型预测即时状态演化，实现精确局部安全引导。实验表明，hint²克服了现有LTL引导扩散方法的局限，在CALVIN基准上优于已有推理时调制方法，并更优雅地完成包含复杂活度与安全约束的指令。最后，我们验证了hint²可在真实UR5e机械臂上执行复杂指令。
原文摘要 · Abstract (English)
A central goal of robot learning is to enable robots to execute rich instructions specified at runtime. Large-scale language-conditioned policies have made substantial progress toward this goal, yet still struggle with temporal structure and safety constraints. Linear Temporal Logic (LTL) provides a powerful language to express complex, non-Markovian instructions. However, guiding learned manipulation policies toward LTL satisfaction remains challenging because modern policies generate short-horizon action chunks and replan in closed loop, while almost all LTL specifications are evaluated over long-horizon trajectories. In this paper, we introduce hint$^2$, a method for guiding short-horizon policies toward satisfying complex LTL specifications at inference time using hierarchical world models. Our key idea is to derive two separate guidance objectives using each world model's abstraction level. A high-level model predicts future action-induced transitions in task-relevant atomic propositions to guide progress through the LTL automaton, while a low-level dynamics model predicts immediate state evolution for accurate local safety guidance. Our results show that hint$^2$ overcomes the limitations of current LTL-guided diffusion methods, outperforms existing inference-time steering methods in CALVIN, and successfully completes instructions with complex liveness and safety constraints more elegantly than language-conditioned alternatives. Finally, we demonstrate that hint$^2$ can handle complex instructions on a real UR5e manipulator.
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