Eric Eaton (University of Pennsylvania): “Lifelong Learning for Autonomous Systems: Progress and Challenges”

Research in lifelong or continual machine learning has advanced rapidly over the past few years, primarily focusing on enabling learned models to acquire new tasks over time while avoiding catastrophic forgetting of previous tasks. However, autonomous systems still lack the ability to rapidly learn new generalizable skills by building upon and continually refining their learned knowledge—a hallmark of lifelong learning in humans and animals. In this talk, I will examine our progress toward lifelong machine learning and its application to autonomous systems, exploring recent work in compositional representations and self-supervision. We’ll then discuss how far we have come and the open problems that remain toward learning generalizable skills in lifelong settings.

Eric Eaton is a research associate professor in the Department of Computer and Information Science at the University of Pennsylvania, and a member of the GRASP (General Robotics, Automation, Sensing, & Perception) lab. He also has a secondary appointment in biomedical and health informatics at Children’s Hospital of Philadelphia. His primary research interests lie in the field of machine learning and interactive AI, with applications to service robotics and personalized medicine. In particular, his research focuses on developing versatile AI systems that can learn multiple tasks over a lifetime of experience in complex environments, transfer learned knowledge to rapidly acquire new abilities, and collaborate effectively with humans and other agents through interaction.