As machine learning enters the mainstream, consumers may assume that it can solve almost any problem. This is not true, says Bruce Lee, a doctoral student in Penn Engineering’s Department of Electrical and Systems Engineering. Lee’s research works to identify how robotic systems learn to perform different tasks, focusing on how to tell when a problem may be too complex—and what to do about it.
Lee, who is advised by Nikolai Matni, assistant professor in electrical and systems engineering and member of the Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE) Center, studies how robotic systems learn from data, with the goal of understanding when robots struggle to learn a dynamic system, and what approaches might be effective at combating those challenges.
His work offers insights into the fundamental limits of machine learning, guiding the development of new algorithms and systems that are both data-efficient and robust.
“When I try to apply a reinforcement learning or imitation learning algorithm to a problem, I often reach a point where it does not work, and I have no idea why,” says Lee. “Is it a bug in my code? Should I just collect more data or run more iterations? Do I need to change the hyperparameters? Sometimes, the answer is none of the above. Rather, the problem is impossible to learn effectively, no matter what learning algorithm I use. My work can help researchers understand when this is the case.”
Improving the way robotic systems learn from data enhances the safety and efficiency of self-driving cars, enabling them to make more reliable decisions in complex, dynamic environments. Similarly, robots operating in human environments, such as in health care or manufacturing, can become more adaptable and capable of performing a wider range of tasks with minimal human intervention. Ultimately, the goal is to create robotic systems that can better serve humanity, contributing to advancements in various fields including transportation, health care, and beyond.
Read more at Penn Engineering Today.
