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.

翻译：机器人基座模型在跨任务与跨环境执行自然语言指令方面展现出强大能力，但其本质仍属数据驱动，缺乏对部署过程中时间依赖性规约的安全性保障与形式化约束。实际应用中，机器人常需满足涉及丰富时空需求的运行约束，包括时间限界目标访问、序列化目标以及持续性安全条件等。本文提出一种面向规约感知的动作分布优化框架，可在不修改预训练机器人基座模型参数的前提下，强制执行部署过程中的广泛信号时序逻辑（STL）约束。该方法在每个决策步骤中，通过前向动力学传播对剩余时间域进行推理，计算满足严格STL可行性约束的最小修正动作分布。我们采用最先进的机器人基座模型，在多种环境与复杂规约场景下通过仿真验证了该框架的有效性。