Safety and stability are common requirements for robotic control systems; however,
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …

When designing controllers for safety-critical systems, practitioners often face a challenging
tradeoff between robustness and performance. While robust control methods provide …

We propose new methods to synthesize control barrier function (CBF) based safe controllers
that avoid input saturation, which can cause safety violations. In particular, our method is …

Safety is a critical concern when applying reinforcement learning (RL) to real-world control
tasks. However, existing safe RL works either only consider expected safety constraint …

Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …

We propose a framework for safe reinforcement learning that can handle stochastic
nonlinear dynamical systems. We focus on the setting where the nominal dynamics are …

Reinforcement learning is a promising approach to synthesizing policies for challenging
robotics tasks. A key problem is how to ensure safety of the learned policy-eg, that a walking …

The lack of stability guarantee restricts the practical use of learning-based methods in core
control problems in robotics. We develop new methods for learning neural control policies …

Reinforcement Learning (RL) algorithms have found limited success beyond simulated
applications, and one main reason is the absence of safety guarantees during the learning …

We provide a framework for incorporating robustness--to perturbations in the transition
dynamics which we refer to as model misspecification--into continuous control …