Constrained Polynomial Zonotopes

We introduce constrained polynomial zonotopes, a novel non-convex set representation that is closed under linear map, Minkowski sum, Cartesian product, convex hull, intersection, union, and quadratic as well as higher-order maps. We show that the computational complexity of the above-mentioned set operations for constrained polynomial zonotopes is at most polynomial in the representation size. The fact that constrained polynomial zonotopes are generalizations of zonotopes, polytopes, polynomial zonotopes, Taylor models, and ellipsoids, further substantiates the relevance of this new set representation. The conversion from other set representations to constrained polynomial zonotopes is at most polynomial with respect to the dimension

Fully-Automated Verification of Linear Systems Using Inner- and Outer-Approximations of Reachable Sets

Reachability analysis is a formal method to guarantee safety of dynamical systems under the influence of uncertainties. A major bottleneck of all reachability algorithms is the requirement to adequately tune certain algorithm parameters such as the time step size, which requires expert knowledge. In this work, we solve this issue with a fully-automated reachability algorithm that tunes all algorithm parameters internally such that the reachable set enclosure satisfies a user-defined accuracy in terms of distance to the exact reachable set. Knowing the distance to the exact reachable set, an inner-approximation of the reachable set can be efficiently extracted from the outer-approximation using the Minkowski difference. Finally, we propose a novel verification algorithm that automatically refines the accuracy of the outer- and inner-approximation until specifications given by time-varying safe and unsafe sets can either be verified or falsified. The numerical evaluation demonstrates that our verification algorithm successfully verifies or falsifies benchmarks from different domains without any requirement for manual tuning.Comment: 16 page

Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes

We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation functions. In particular, we abstract the input-output relation of each neuron by a polynomial approximation, which is evaluated in a set-based manner using polynomial zonotopes. While our approach can also can be beneficial for open-loop neural network verification, our main application is reachability analysis of neural network controlled systems, where polynomial zonotopes are able to capture the non-convexity caused by the neural network as well as the system dynamics. This results in a superior performance compared to other methods, as we demonstrate on various benchmarks

Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes

While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critical systems. In this work, we address this issue by a safety shield for nonlinear continuous systems that solve reach-avoid tasks. Our safety shield prevents applying potentially unsafe actions from a reinforcement learning agent by projecting the proposed action to the closest safe action. This approach is called action projection and is implemented via mixed-integer optimization. The safety constraints for action projection are obtained by applying parameterized reachability analysis using polynomial zonotopes, which enables to accurately capture the nonlinear effects of the actions on the system. In contrast to other state-of-the-art approaches for action projection, our safety shield can efficiently handle input constraints and dynamic obstacles, eases incorporation of the spatial robot dimensions into the safety constraints, guarantees robust safety despite process noise and measurement errors, and is well suited for high-dimensional systems, as we demonstrate on several challenging benchmark systems

ARCH-COMP19 Category Report: Continuous and hybrid systems with nonlinear dynamics

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2019. In this year, 6 tools Ariadne, CORA, DynIbex, Flow*, Isabelle/HOL, and JuliaReach (in alphabetic order) participated. They are applied to solve reachability analysis problems on four benchmark problems, one of them with hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools