嘉宾介绍

石冠亚

就职于卡内基梅隆大学计算机学院机器人研究所，任助理教授。2022年在加州理工学院CMS系取得博士学位。研究方向为机器学习与控制理论的结合及其在机器人控制与智能决策中的应用。在Science Robotics，IEEE T-RO，NeurIPS，ICRA，ACC，L4DC等机器人、机器学习、控制领域的顶级期刊与会议发表论文二十余篇。曾先后获得加州理工学院Simoudis探索奖、Ben P.C. Chou博士论文奖及芝加哥大学数据科学明日之星奖等。

内容抢先读

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.