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AIC Seminar Series

Autonomous Sensor and Action Model Learning for Mobile Robots

Daniel StrongerUniversity of Texas at Austin[Home Page]

Notice:  hosted by Charlie Ortiz

Date:  2008-04-22 at 16:00

Location:  EJ228 (SRI E building)  (Directions)

   Abstract

In order for a mobile robot to accurately interpret its sensations and predict the effects of its decisions, it must have accurate models of its sensors and actions. These models are typically tuned manually, a brittle and laborious process. Autonomous model learning is a promising alternative to manual calibration, but previous work has assumed the presence of an accurate action or sensor model in order to train the other model. This talk presents a novel methodology to enable mobile robots to learn both their action and sensor models, starting without an accurate version of either. This methodology is based on an adaptation of the Expectation-Maximization (EM) algorithm, where the learned parameters are the action and sensor models. The resulting technique is validated experimentally on a Sony Aibo ERS-7 robot.

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