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Learning Portable Representations for High-Level Planning
Steven James · Benjamin Rosman · George Konidaris

Tue Jul 14 12:00 PM -- 12:45 PM & Tue Jul 14 11:00 PM -- 11:45 PM (PDT) @

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are provably sufficient for planning. We demonstrate transfer in two different domains, where an agent learns a portable, task-independent symbolic vocabulary, as well as operators expressed in that vocabulary, and then learns to instantiate those operators on a per-task basis. This reduces the number of samples required to learn a representation of a new task.

Author Information

Steven James (University of the Witwatersrand)
Benjamin Rosman (University of the Witwatersrand, South Africa)

Benjamin Rosman received a Ph.D. degree in Informatics from the University of Edinburgh in 2014. Previously, he obtained an M.Sc. in Artificial Intelligence from the University of Edinburgh, a Bachelor of Science (Honours) in Computer Science from the University of the Witwatersrand, South Africa, and a Bachelor of Science (Honours) in Computational and Applied Mathematics, also from the University of the Witwatersrand. He is an Associate Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand. He is the Chair of the IEEE South African joint chapter of Control Systems, and Robotics and Automation. Prof. Rosman’s research interests focus primarily on learning and decision making in autonomous systems, in particular studying how learning can be accelerated through abstracting and generalising knowledge gained from solving previous problems. He additionally works in the area of skill and behaviour learning for robots.

George Konidaris (Brown)

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