Rapid Concept Learning for Mobile

Concept learning in robotics is an extremely challenging problem. Sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce feature detectors such as \opening" or \wall". The second task is recycling, where the robot has to learn to recognize objects, such as a \trash can". We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom aaorded by such an approximator, we show the two strategies provide suucient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the eeectiveness of this approach.