嘉賓介紹

石冠亞

就職于卡內基梅隆大學計算機學院機器人研究所，任助理教授。2022年在加州理工學院CMS系取得博士學位。研究方向為機器學習與控制理論的結合及其在機器人控制與智能決策中的應用。在Science Robotics，IEEE T-RO，NeurIPS，ICRA，ACC，L4DC等機器人、機器學習、控制領域的頂級期刊與會議發表論文二十余篇。曾先后獲得加州理工學院Simoudis探索獎、Ben P.C. Chou博士論文獎及芝加哥大學數據科學明日之星獎等。

內容搶先讀

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.