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arXiv · 2304.14886

Ensuring Reliable Robot Task Performance through Probabilistic Rare-Event Verification and Synthesis

Guy Scher·Sadra Sadraddini·Ariel Yadin·Hadas Kress-Gazit
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Abstract

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.

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Guy Scher, Sadra Sadraddini, Ariel Yadin, Hadas Kress-Gazit. 2023-04-28. Ensuring Reliable Robot Task Performance through Probabilistic Rare-Event Verification and Synthesis. https://arxiv.org/abs/2304.14886

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