让机器人模型在执行任务时自动满足时间约束，不修改模型本身。
Specification-Aware Distribution Shaping for Robotics Foundation Models
- 基于信号时序逻辑（STL）约束，动态优化动作分布以满足时空要求。
- 在多个复杂场景中验证，确保机器人按指定时间完成目标访问与顺序任务。
- 适用于需高安全性与严格时序控制的工业机器人部署场景。
机器人基础模型在跨任务和环境执行自然语言指令方面表现出强大能力，但其仍高度依赖数据驱动，部署时缺乏对安全性和时变规范满足性的形式化保障。实际应用中，机器人常需遵守包含丰富时空要求的操作约束，如限时到达目标、顺序目标达成及持续安全条件等。本文提出一种规范感知的动作分布优化框架，在不修改预训练机器人基础模型参数的前提下，强制执行广泛类别的信号时序逻辑（STL）约束。在每个决策步骤，该方法通过前向动力学传播推演剩余时域，计算出满足硬性STL可行性约束的最小修改动作分布。我们在多个环境中使用最先进机器人基础模型进行仿真验证，涵盖复杂规范下的执行表现。
原文摘要 · Abstract (English)
Robotics foundation models have demonstrated strong capabilities in executing natural language instructions across diverse tasks and environments. However, they remain largely data-driven and lack formal guarantees on safety and satisfaction of time-dependent specifications during deployment. In practice, robots often need to comply with operational constraints involving rich spatio-temporal requirements such as time-bounded goal visits, sequential objectives, and persistent safety conditions. In this work, we propose a specification-aware action distribution optimization framework that enforces a broad class of Signal Temporal Logic (STL) constraints during execution of a pretrained robotics foundation model without modifying its parameters. At each decision step, the method computes a minimally modified action distribution that satisfies a hard STL feasibility constraint by reasoning over the remaining horizon using forward dynamics propagation. We validate the proposed framework in simulation using a state-of-the-art robotics foundation model across multiple environments and complex specifications.
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