Recent breathtaking advances in machine learning beckon to their applications in a wide range of 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. In particular, I will introduce a concrete example in such a unified framework called Neural-Control Family, a family of deep-learning-based nonlinear control methods with not only stability and robustness guarantees but also new capabilities in agile robotic control. For example, Neural-Swarm enables close-proximity flight of a drone swarm and Neural-Fly enables precise drone control in strong time-variant wind conditions.
