报告题目:Neural-Control Family: Safe Agile Deep-learning-based Robotic Control in Dynamic Environments
报告人:石冠亚  博士

讲师简介:
石冠亚,目前在加州理工学院CMS系攻读博士学位,他将于2023年入职卡内基梅隆大学计算机学院机器人研究所担任助理教授。在此之前,他于2017年毕业于清华大学,并于2020年在英伟达
担任访问研究员。他的学术兴趣是机器学习与控制理论的结合,以及在机器人控制与智能决策中的应用。他在Science Robotics,IEEE T-RO,NeurIPS,ICRA,ACC,L4DC等机器人、机器
学习、控制方面的顶级期刊与会议发表论文二十余篇。他先后获得了加州理工学院Simoudis探索奖和Ben P.C. Chou博士论文奖,以及芝加哥大学数据科学明日之星奖。

报告摘要:
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.

讲座时间:2022年8月16日  周二 10:00
线上腾讯会议
主持人;许超(浙江大学湖州研究院院长)

By ccxu

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