Real Robots, Real Learning Problems

Abstract

The weaknesses of existing learning techniques, and the variety of knowledge necessary to make a robot perform efficiently in the real world, suggest that many concurrent, complementary, and redundant learning methods are necessary. We propose a division of learning styles into four main types based on the amount of built-in structure and the type of information being learned. Using this classification, we discuss the effectiveness of various learning methodologies when applied in a real robot context.