Award-Winning Researcher Trains Robots to Make Educated Guesses
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Liz Wegerer is a freelance writer based in Seattle.
IEEE Member Yen-Ling Kuo, an assistant professor of computer science at the University of Virginia in Charlottesville, and one of her students test the dataset aggregation method on a tactile-force-controlled robotic gripper. Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world.
During that time, she was introduced to Logo, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation. In high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.
“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.” Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences. Kuo, now an assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award.
The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers. Kuo’s winning paper, “Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.
She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.
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