Leslie Kaelbling: Rich Representations for Rational Robots

Title: Rich Representations for Rational Robots

Abstract:

For robots to operate flexibly and intelligently in complex domains over long horizons, they will need to learn, represent and “reason” with information about objects, space, physics, geometry, and people. They will need to represent their own uncertainty as well as possibly the beliefs and objectives of others, and the ways in which their own sensors and effectors connect to external reality. There is unlikely to be one representation that will serve all these purposes. I’ll share some high-level ideas about, and some concrete technical progress toward, an approach that builds and uses multiple representations, creating them dynamically to address important subproblems as they arise.

Bio:

Leslie is a Professor in EECS at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founder of the Journal of Machine Learning Research.

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