Learning landmark triples by experimentation

Abstract

This article describes a method for learning a set of landmarks suitable for place navigation. The approach is novel in that it exploits the ability of a robot to learn through active perception in the task environment, similar to the learning by experimentation technique developed for LEX (Mitchell et al., 1990). The proposed strategy uses heuristics to select and rank candidate triples, then generates test cases to confirm that the best triple is sufficient. The method supports the use of multiple sensors with different computational and energy costs, where a utility function captures the tradeoff between navigational performance ranking and cost. Over 100 data points were collected on a mobile robot using a laser barcode reader and computer vision to identify landmarks. The results indicated that active perception and experimentation identified triples with better navigational properties. Furthermore, the learning process is proactive: it was shown to prevent the robot from learning a triple which was not visible over the entire navigational space and/or was not sufficient in practice.