This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

We present an historical overview about the connections between the analysis of risk and
the control of autonomous systems. We offer two main contributions. Our first contribution is …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

We present a model-free reinforcement learning algorithm to synthesize control policies that
maximize the probability of satisfying high-level control objectives given as Linear Temporal …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

One of the biggest boosts for innovation in engineering has been the ongoing improvement
of digital processor technology. Connections between physical systems and computing …

Ensuring safety is important for the practical deployment of reinforcement learning (RL).
Various challenges must be addressed, such as handling stochasticity in the environments …

Deep neural network controllers for autonomous driving have recently benefited from
significant performance improvements, and have begun deployment in the real world. Prior …

One of the primary challenges for autonomous robotics in uncertain and dynamic
environments is planning and executing a collision-free path. Hybrid dynamic obstacles …

Safety analysis is a necessary component in the design and deployment of autonomous
robotic systems. Techniques from robust optimal control theory, such as Hamilton-Jacobi …