Learning more from less data: experiments with lifelong robot learning

Abstract

Learning more accurate functions from less data is a key issue in robot learning. This paper investigates robot learning in a lifelong learning framework. In lifelong learning, the learner faces an entire collection of learning tasks, not just a single one. Thus, it provides the opportunity for synergy among multiple tasks. To obtain this synergy, the central question in lifelong learning is how can the learner transfer knowledge across multiple tasks. In this paper we describe a selective approach to lifelong learning, the task clustering (TC) algorithm. TC transfers knowledge across multiple tasks by adjusting the distance metric in nearest neighbour generalization. To increase robustness to unrelated tasks, TC arranges all learning tasks hierarchically. When a new learning task arrives, TC relates it to the task hierarchy, in order to transfers knowledge selectively from the most related tasks only. As a result, TC is more robust than its unselective counterpart. Thus far, TC has been successfully applied to perception tasks involving visual and ultrasonic input, using our mobile robot XAVIER. (3 pages)