Recent breathtaking advances in machine learning beckon to their applications in a wide range of real-world autonomous systems. However, for safety-critical settings such as agile robotic control in hazardous environments, we must confront several key challenges before widespread deployment. Most importantly, the learning system must interact with the rest of the autonomous system (e.g., highly nonlinear and non-stationary dynamics) in a way that safeguards against catastrophic failures with formal guarantees. In addition, from both computational and statistical standpoints, the learning system must incorporate prior knowledge for efficiency and generalizability.

In this talk, I will present progress towards establishing a unified framework that fundamentally connects learning and control. First, I motivate the benefit and necessity of such a unified framework by the Neural-Control Family, a family of nonlinear deep-learning-based control methods with not only stability and robustness guarantees but also new capabilities in agile robotic control. Then I will discuss two unifying interfaces between learning and control: (1) meta-adaptive control, and (2) competitive predictive control. Both interfaces yield settings that jointly admit both learning-theoretic and control-theoretic guarantees.