Control for throwing manipulation by one joint robot
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot''s performance of a wide range of dynamic tasks. We have developed {\it task-level learning} that successfully improves a robot''s performance of two complex tasks, ball-throwing and juggling. With task- level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.