Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
This work addresses task optimization in robotics, but it appears incremental as it focuses on evaluating existing metrics and methods without introducing new techniques.
The paper evaluated Signal Temporal Logic (STL) robustness metrics for optimizing robotic manipulation tasks, showing how STL-based cost functions can be optimized using off-the-shelf methods in a simulated planar environment.
Signal Temporal Logic (STL) is an efficient technique for describing temporal constraints. It can play a significant role in robotic manipulation, for example, to optimize the robot performance according to task-dependent metrics. In this paper, we evaluate several STL robustness metrics of interest in robotic manipulation tasks and discuss a case study showing the advantages of using STL to define complex constraints. Such constraints can be understood as cost functions in task optimization. We show how STL-based cost functions can be optimized using a variety of off-the-shelf optimizers. We report initial results of this research direction on a simulated planar environment.