Event Details
Learning Robots: from Case-Based Reasoning to Deep Learning
Susan Fox is DeWitt Wallace Professor of Computer Science at Macalester College in St. Paul, Minnesota. Fox graduated from Oberlin College in 1990 with an AB in computer science, and in 1995 earned a PhD in computer science, with a graduate minor in cognitive science, from Indiana University. That year, she took up a tenure-track position at Macalester.
Fox’s research interests lie in artificial intelligence and machine learning, specifically in the creation of computer systems that can improve themselves based on their experiences. At Macalester, she began working with robot navigation, creating systems to enable a robot to plan, navigate, and learn in the hallways of Olin-Rice. Student researchers have been the heart of Fox’s research program. She has collaborated with more than twenty-five students on summer research projects over the past twenty-two years. Fox has received numerous summer research awards from Macalester to support student research. In 2009 she was co-principal investigator on an NSF Scholarships for STEM grant that supported computer science students with scholarships and summer research funding. And in 2016 Fox was co-PI on a grant from the Clare Boothe Luce Foundation to support women in computer science; this grant also supported multiple summer research students.
Together with her students, she has developed hybrid robot control architectures that integrate separate components for different aspects of the robot’s tasks. At the bottom, low-level reactive control manages the robot's moment-to-moment movement, ensuring that it avoids colliding with obstacles and moves reliably from place to place. Above that, path planning develops multistep plans for how to navigate from where the robot is to its next goal location. And localization determines where the robot is at all times in the face of inaccurate movements and sensor inputs. Fox’s research has integrated machine learning with each of these tasks, to improve their performance. Her current research focuses on machine learning to improve the difficult localization task.
In 2019, Fox received the Jack and Marty Rossmann Excellence in Teaching Award. She finds teaching, at all levels of the curriculum, to be the most rewarding aspect of her professional life. Introducing students to big ideas of computer science, from programming concepts to the underlying theory, is a joyful experience. Fox has supervised many capstone projects by students in her advanced courses on artificial intelligence and robotics. These innovative projects constantly push her to learn new techniques and tools; she says, “learning alongside students is the most exciting feeling.”
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