Autonomous Sensor and Action Model Learning for Mobile Robots
| Daniel Stronger | University of Texas at Austin | [Home Page] |
Notice: hosted by Charlie Ortiz
Date: Tuesday April 22, 2008 at 16:00
Location: EJ228 (SRI E building) (Directions)
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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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