Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Abstract Control Barrier Functions (CBFs) are a popular approach for safe control of
nonlinear systems. In CBF-based control, the desired safety properties of the system are …

Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …

We study the problem of learning controllers for discrete-time non-linear stochastic
dynamical systems with formal reach-avoid guarantees. This work presents the first method …

The rapid advance of deep reinforcement learning techniques enables the oversight of
safety-critical systems through the utilization of Deep Neural Networks (DNNs). This …

Control barrier functions (CBFs) are important in safety-critical systems and robot control
applications. Neural networks have been used to parameterize and synthesize CBFs with …

This paper presents a novel approach to synthesize positive invariant sets for unmodeled
nonlinear systems using direct data-driven techniques. The data-driven invariant sets are …

Verifying the performance of safety-critical, stochastic systems with complex noise
distributions is difficult. We introduce a general procedure for the finite abstraction of …

This paper presents Fossil 2.0, a new major release of a software tool for the synthesis of
certificates (eg, Lyapunov and barrier functions) for dynamical systems modelled as ordinary …

Control Barrier Functions (CBFs) that provide formal safety guarantees have been widely
used for safety-critical systems. However, it is non-trivial to design a CBF. Utilizing neural …