Task-level robot learning

摘要

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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>