Task-level robot learning
Task-level robot learning
复制标题
任务级机器人学习
DOI：
10.1109/robot.1988.12245
复制
发表时间：
1988
期刊：
影响因子：
--
通讯作者：
D. Reinkensmeyer
中科院分区：
文献类型：
--
作者：
E. Aboaf;C. Atkeson;D. Reinkensmeyer
The functionality of robots can be improved by programming them to learn tasks from practice. Task-level learning can compensate for the structural modeling errors of the robot's lower-level control systems and can speed up the learning process by reducing the degrees of freedom of the models to be learned. The authors demonstrate two general learning procedures-fixed-model learning and refined-model learning-on a ball-throwing robot system. Both learning approaches refine the task command based on the performance error of the system, while they ignore the intermediate variables separation the lower-level systems. The authors also provide experimental and theoretical evidence that task-level learning can improve the functionality of robots.<<ETX>>
