A Mobile Robot That Learns Its Place
Sageev Oore, Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto, Canada
Gregory Dudek
School of Computer Science
McGill University
Montreal, Canada
Abstract
We show how a neural network can be used to allow a mobile robot to
derive an accurate estimate of its location from noisy sonar sensors and noisy motion
information. The robot's model of its location is in the form of a probability
distribution across a grid of possible locations. This distribution is updated using both
the motion information and the predictions of a neural network that maps locations into
likelihood distributions across possible sonar readings. By predicting sonar readings from
locations, rather than vice versa, the robot can handle the very nongaussian noise in the
sonar sensors. By using the constraint provided by the noisy motion information, the robot
can use previous readings to improve its estimate of its current location. By treating the
resulting estimates as if they were correct, the robot can learn the relationship between
location and sonar readings without requiring an external supervision signal that
specifies the actual location of the robot. It can learn to locate itself in a new
environment with almost no supervision, and it can maintain its location ability even when
the environment is nonstationary.
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Neural Computation (1997) 9:3 Pages 683-699
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