Learning Robots
Compared to present industrial robots future robots, especially service and personal robots, will need much more intelligence, robustness and userfriendliness. The ability to learn contributes to these characteristics and is, therefore, becoming more and more important. Here three of the numerous varieties of learning are discussed together with results of real-world experiments with three autonomous robots: (1) the acquisition of map knowledge by a mobile robot, allowing it to navigate in a network of corridors, (2) the acquisition of motion control knowledge by a calibration-free manipulator, allowing it to gain task-related experience and improve its manipulation skills while it is working, and (3) a humanoid robots ability to learn how to perform service tasks in an initially unknown environment through dialogues with initially unknown and untrained users.
Volker Graefe Rainer Bischoff
Intelligent Robots Laboratory Bundeswehr University Munich 85577 Neubiberg,Germany
国际会议
厦门
英文
1-9
2010-05-22（万方平台首次上网日期，不代表论文的发表时间）