Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems

arXiv:2010.08311v4 Announce Type: replace-cross
Abstract: This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural networks for processing sensory input. …

applications arxiv cs.cr cs.ro current design focus implementation location resilience resilient robotics robustness speed state systems verification