首页 | 本学科首页   官方微博 | 高级检索  
     


Rapid Concept Learning for Mobile Robots
Authors:Mahadevan  Sridhar  Theocharous  Georgios  Khaleeli  Nikfar
Abstract: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 features such as ldquoopeningrdquo or ldquowallrdquo. The second task is recycling, where the robot has to learn to recognize objects, such as a ldquotrash canrdquo. 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 afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.
Keywords:robot learning  concept learning  neural networks
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号