Bruce Lee, a doctoral student in Penn Engineering’s department of electrical and systems engineering, 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.

Mr. Lee, who is advised by Nikolai Matni, an assistant professor of 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.

Ultimately, the goal is to create robotic systems that can better serve humanity, contributing to advancements in various fields including transportation, healthcare, and beyond. One case study that Mr. Lee is currently considering is a project by Google that aims to help robots learn general control policies from data. The generalist policies are intended to help robots perform new tasks with a limited amount of training data by leveraging similarities to tasks that have been conducted during the training phase.

“New results in machine learning, such as ChatGPT, Midjourney, diffusion models or deep learning in general, are very exciting and are enabling new capabilities we haven’t seen before,” said Dr. Matni. “However, despite this exciting progress, they are still unreliable and data-hungry. While this is not a problem when applied to chatbots or image generation, it can be catastrophic when applied to safety-critical systems that interact with the physical world, such as self-driving cars.”

One key takeaway from the research, Mr. Lee said, is that sometimes the problem is just too difficult. Control system engineers and researchers often think their job is to design an effective control system for a specific system facing a specific challenge, but this isn’t always the right approach. Mr. Lee’s results can also help to guide the design of systems that are easier to control.

Mr. Lee, who is expected to graduate in 2025, is also studying how researchers and practitioners can work around the fundamental limits of what robotic systems can do. One approach to doing so is strategically designing systems to make them as easy to learn as possible. Another is to supplement the data collected from any system of interest with data from related systems, leveraging the similarity between the two to continue to learn while using less data from the system of interest. “Many outside the field think machine learning can solve almost anything. My work helps to show that it cannot,” said Mr. Lee. “Our results show that if we have complicated systems with a high number of states, then learning an adequate control system from scratch will require an exorbitant amount of data to be collected from the world, which may be impossible for physical robotic systems.”

Adapted from a Penn Engineering Today article by Liz Wai-Ping Ng, June 10, 2024.