Ro-To-Go! Robust Reactive Control with Signal Temporal Logic
Roland Ilyes, Lara Brudermüller, Nick Hawes, Bruno Lacerda
AI summary
Problem
Traditional Signal Temporal Logic robustness used in model predictive control depends on the robot's entire history, causing future plans to be unfairly limited by past proximity to obstacles and hindering reactive behavior.
Approach
The authors introduce robustness-to-go, a quantitative semantics that scores only the future suffix of a trajectory, and compute it efficiently online using formula progression within an MPC loop.
Key results
- Introduced robustness-to-go, a suffix-focused STL robustness measure
- Proved soundness and established equivalence to formula progression for efficient online evaluation
- Integrated the metric into an MPC algorithm using VP-STO and CMA-ES optimization
- Demonstrated improved safety and robustness over traditional and AGM baselines in simulation
Why it matters
Enables autonomous robots to maintain robust, reactive control in dynamic environments by decoupling future planning from past constraints, advancing formal methods for real-time robotics.
Abstract
Signal Temporal Logic robustness is a common objective for optimal robot control, but its dependence on history limits the robot’s decision-making capabilities when used in model predictive control approaches. In this work, we introduce Signal Temporal Logic robustness-to-go, a new quantitative semantics for the logic that isolates the contributions of suffix trajectories. We prove its relationship to formula progression for Metric Temporal Logic, and show that the robustness-to-go depends only on the suffix trajectory and progressed formula. We implement robustness-to-go as the objective in a model predictive control algorithm and use formula progression to efficiently evaluate it online. We test the algorithm in simulation and compare it to model predictive control using other robustness measures. Our experiments show that using robustness-to-go improves performance compared to using traditional robustness.