Real-time learning of resolved velocity control on a Mitsubishi PA-10

Abstract— Learning inverse kinematics has long been fascinating the robot learning community. While humans acquire this transformation to complicated tool spaces with ease, it is not a straightforward application for supervised learning algorithms due to non-convex learning problem. However, the key insight that the problem can be considered convex in small local regions allows the application of locally linear learning methods. Nevertheless, the local solution of the problem depends on the data distribution which can result into inconsistent global solutions with large model discontinuities. While this problem can be treated in various ways in offline learning, it poses a serious problem for online learning. Previous approaches to the real-time learning of inverse kinematics avoid this problem using smart data generation, such as the learner biasses its own solution. Such biassed solutions can result into premature convergence, and from the resulting solution it is often hard to un...

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Jan Peters, Duy Nguyen-Tuong
• Keywords: ICRA 2008, Inverse Kinematics, Learning Algorithm, Non-convex Learning Problem, Robotics