Research

My research develops machine learning and AI for versatile systems that are deployed for extended durations in complex dynamic environments. This research combines and integrates techniques from continual or lifelong learning, transfer and multi-task learning, deep learning, reinforcement learning, multimodal learning, collaborative AI, machine perception, and robotic control. We apply these methods to problems in autonomous robotics and precision medicine.

Details of my research on these topics can be found on my research and publications pages.

Featured Current Projects

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Autonomous Search-and-Triage Robots for the DARPA Triage Challenge

Compositional Continual Learning for Open Worlds

Robust and Replicable Reinforcement Learning

Ambient AI for Precision Medicine

Multimodal Clinical Foundation Models

Autonomous Mobile Service Robots

Learning Affordances from Visual Observation

AI Education

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About Me

Eric Eaton is a research associate professor in the Department of Computer and Information Science at the University of Pennsylvania, and a faculty member of the General Robotics Automation, Sensing, Perception (GRASP) lab. He also holds secondary appointments in the Department of Biomedical and Health Informatics at Children’s Hospital of Philadelphia, as a senior fellow in the Penn Institute for Biomedical Informatics, and as a technical advisor for Penn Health-Tech.

His primary research interests lie in the fields of machine learning and AI with applications to robotics and precision medicine. In particular, his research focuses on developing versatile AI systems that can be deployed for extended durations in complex dynamic environments. These systems learn multiple tasks over a lifetime of experience, transfer learned knowledge to rapidly acquire new abilities, and collaborate effectively with humans and other agents. This research combines and integrates techniques from continual or lifelong learning, transfer and multi-task learning, deep learning, reinforcement learning, multimodal learning, collaborative AI, machine perception, and robotic control. He is also an international leader in AI Education, most notably through his work as chair of the AI subcommittee that developed the ACM/IEEE/AAAI Computer Science 2023 curriculum (CS 2023), which is used by institutions worldwide.

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