Results are presented from a preliminary investigation of task-level learning, an approach to learning from practice. The authors programmed a robot to juggle a single ball in three dimensions by batting it upwards with a large paddle. The robot uses a real-time binary vision system to track the ball and measure its performance. Task-level learning consists of building a model of performance errors at the task level during practice, and using that model to refine task-level commands. A polynomial surface was fitted to the errors in the path which the ball took after each hit, and this task model is used to refine how the ball is hit. This application of task-level learning dramatically increased the number of consecutive hits the robot could execute before the ball was hit out of range of the paddle.>

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.>

Robot manipulator motions near the wrist degeneracy often cause excessive joint rates that cannot be satisfied by the joint actuators and thus result in both position and orientation errors. We have developed an inverse Jacobian control policy that bounds the excessive rates while guaranteeing the position accuracy and minimizing the orientation error of the end effector.