Modelling and Verifying Robotic Software that Uses Neural Networks

Verifying learning robotic systems is challenging. Existing techniques and tools for verification of an artificial neural network (ANN) are concerned with component-level properties. Here, we deal with robotic systems whose control software uses ANN components, and with properties of that software that may depend on all components. Our focus is on trained fully connected ReLU neural networks for control. We present an approach to (1) modelling ANN components as part of behavioural models for control software and (2) verification using traditional and ANN-specific verification tools. We describe our results in the context of RoboChart, a domain-specific modelling language for robotics with support for formal verification. We describe our modelling notation and a strategy for automated proof using Isabelle and Marabou, for example.