Lazy Acquisition of Place Knowledge

Authors:
Pat Langley;Karl Pfleger;Mehran Sahami
Affiliations:
Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306. E-mail: Langley@cs.stanford.edu;Knowledge Systems Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305. E-mail: KPfleger@hpp.stanford.edu;Robotics Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305. E-mail: sahami@cs.stanford.edu
Venue:
Artificial Intelligence Review - Special issue on lazy learning
Year:
1997

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Abstract

In this paper we define the task of place learning and describe oneapproach to this problem. Our framework represents distinct places as evidence grids, a probabilistic description of occupancy. Placerecognition relies on nearest neighbor classification, augmented by a registration process to correct for translational differences between the two grids. The learning mechanism is lazy in that it involves the simple storage of inferred evidence grids. Experimental studieswith physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensornoise, that it benefits from improved quality in stored cases, and thatit scales well to environments with many distinct places. Additionalstudies suggest that using historical information about the robot‘spath through the environment can actually reduce recognition accuracy. Previous researchers have studied evidence grids and place learning,but they have not combined these two powerful concepts, nor have theyused systematic experimentation to evaluate their methods‘ abilities.