Secure-by-construction synthesis of cyber-physical systems
Correct-by-construction synthesis is a cornerstone of the confluence of formal methods and
control theory towards designing safety-critical systems. Instead of following the time-tested …
control theory towards designing safety-critical systems. Instead of following the time-tested …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Learning safe control for multi-robot systems: Methods, verification, and open challenges
In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
How to train your neural control barrier function: Learning safety filters for complex input-constrained systems
Control barrier functions (CBFs) have become popular as a safety filter to guarantee the
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
FOSSIL: a software tool for the formal synthesis of lyapunov functions and barrier certificates using neural networks
This paper accompanies FOSSIL: a software tool for the synthesis of Lyapunov functions
and of barrier certificates (or functions) for dynamical systems modelled as differential …
and of barrier certificates (or functions) for dynamical systems modelled as differential …
Learning safe, generalizable perception-based hybrid control with certificates
C Dawson, B Lowenkamp, D Goff… - IEEE Robotics and …, 2022 - ieeexplore.ieee.org
Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate
complex environments, but developing certifiably safe feedback controllers around these …
complex environments, but developing certifiably safe feedback controllers around these …
Learning safe neural network controllers with barrier certificates
We provide a new approach to synthesize controllers for nonlinear continuous dynamical
systems with control against safety properties. The controllers are based on neural networks …
systems with control against safety properties. The controllers are based on neural networks …
Unifying qualitative and quantitative safety verification of DNN-controlled systems
The rapid advance of deep reinforcement learning techniques enables the oversight of
safety-critical systems through the utilization of Deep Neural Networks (DNNs). This …
safety-critical systems through the utilization of Deep Neural Networks (DNNs). This …
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the" black box" nature of DRL agents …
agents that control autonomous systems. However, the" black box" nature of DRL agents …