A MOBILE ROBOT THAT LEARNS ITS PLACE

We show how a neural network can be used to allow a mobile robot to de rive 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 location s. This distribution is updated using both the motion information and the predictions of a neural network that maps locations into likelihoo d distributions across possible sonar readings. By predicting sonar re adings from locations, rather than vice versa, the robot can handle th e 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 t he relationship between location and sonar readings without requiring an external supervision signal that specifies the actual location of t he robot. It can learn to locate itself in a new environment with almo st no supervision, and it can maintain its location ability even when the environment is nonstationary.