Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …

Real-world autonomous systems are often controlled using conventional model-based
control methods. But if accurate models of a system are not available, these methods may be …

Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate
complex environments, but developing certifiably safe feedback controllers around these …

Bringing dynamic robots into the wild requires a tenuous balance between performance and
safety. Yet controllers designed to provide robust safety guarantees often result in …

Modern nonlinear control theory seeks to endow systems with properties such as stability
and safety, and has been deployed successfully across various domains. Despite this …

This paper combines episodic learning and control barrier functions (CBFs) in the setting of
bipedal locomotion. The safety guarantees that CBFs provide are only valid with perfect …

Many physical system models exhibit a structural property known as differential flatness.
Intuitively, differential flatness allows us to separate the system's nonlinear dynamics into a …

Real-world autonomous systems are often controlled using conventional model-based
control methods. But if accurate models of a system are not available, these methods may be …