---

Biography

Prof. Qin Lin has been a tenure-track Assistant Professor in the Technology Division of the Cullen College of Engineering at the University of Houston since 2024. He completed his postdoctoral training at the Robotics Institute of Carnegie Mellon University, United States, in 2021, and earned his Ph.D. in Computer Science from Delft University of Technology, the Netherlands, in 2019.

Dr. Lin actively publishes in prestigious conferences and journals in robotics, control, and vehicle technology, including ACC, CDC, IFAC, IROS, ICRA, and various IEEE Transactions.

He serves as an Associate Editor for respected journals such as IEEE Robotics and Automation Letters and IEEE Transactions on Vehicular Technology. His research is currently supported by grants from the National Science Foundation (NSF) and the U.S. Department of Education.

Abstract

As robotic systems become increasingly prevalent in transportation, manufacturing, healthcare, and other domains, ensuring their safe and reliable operation under the three fundamental laws is critical, whether they are driven by traditional control algorithms or enhanced by artificial intelligence.

This talk introduces a unified framework for disturbance rejection, which is foundational for safe and reliable robotic systems. The framework integrates control theory and machine learning to improve the dependability of robots operating in uncertain and dynamic environments.

Although grounded in robotics and systems engineering, the implications extend beyond technical disciplines. By addressing how systems respond to unexpected events such as hardware failures or unpredictable environmental conditions, the work also engages with broader societal concerns about trust in autonomous systems, a key issue in AI and automation ethics and policy.

Understanding and mitigating the limitations of AI and automation is essential for responsibly deploying these technologies in society.