Specification-Aware Distribution Shaping for Robotics Foundation Models
TL;DR
This work proposes a specification-aware action distribution optimization framework that enforces a broad class of Signal Temporal Logic constraints during execution of a pretrained robotics foundation model without modifying its parameters.
Abstract
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 constraint satisfaction 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 state-of-the-art robotics foundation models across multiple environments and complex specifications.
