Providing guarantees on the safe operation of robots against edge cases is challenging as testing methods such as traditional Monte-Carlo require too many samples to provide reasonable statistics. Built upon recent advancements in rare-event sampling, we present a model-based method to verify if a robotic system satisfies a Signal Temporal Logic (STL) specification in the face of environment variations and sensor/actuator noises. Our method is efficient and applicable to both linear and nonlinear and even black-box systems with arbitrary, but known, uncertainty distributions. For linear systems with Gaussian uncertainties, we exploit a feature to find optimal parameters that minimize the probability of failure. We demonstrate illustrative examples on applying our approach to real-world autonomous robotic systems.
@article{arxiv.2304.14886,
title = {Ensuring Reliable Robot Task Performance through Probabilistic Rare-Event Verification and Synthesis},
author = {Guy Scher and Sadra Sadraddini and Ariel Yadin and Hadas Kress-Gazit},
journal= {arXiv preprint arXiv:2304.14886},
year = {2023}
}