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ML Lunch (Nov 18, 2013): Spectral Robotics

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Published on Nov 18, 2013

Speaker: Byron Boots
University of Washington

Abstract
A fundamental challenge in robotics is the so-called ``critter problem:’’ a robot, capable of performing actions and receiving observations, is placed in an unknown environment. The robot has no interpretation for its actions or observations and no knowledge of the structure of the environment. The problem is to program the robot to learn about its observations, actions, and environment well enough to make predictions of future observations given sequences of actions.

This modeling problem is especially challenging given the wide variety of sensors, actuators, and domains that are encountered in modern robotics. As a result, researchers have largely abandoned the critter problem and have instead focused on leveraging extensive domain knowledge to develop special-purpose tools for special cases of the problem: e.g., system identification to learn Kalman filters, body schema learning for manipulators, structure-from-motion in vision, or the many methods for simultaneous localization and mapping.

In this talk I will revisit the original critter problem from a modern machine learning perspective. I will discuss how spectral learning algorithms can unify disparate tools and special cases encountered in different sub-areas of robotics into a single general-purpose toolkit. Finally, I will show how spectral methods have achieved state-of-the-art performance on real-world robotics problems in several different domains.

For more ML Lunch talks, visit http://www.cs.cmu.edu/~learning/

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