Alignment-based transfer learning for robot models

Abstract

Robot manipulation tasks require on robot models. When exact physical parameters of the robot are not available, learning robot models from data becomes an appealing alternative. Most learning approaches are formulated in a supervised learning framework and are based on clearly defined training sets. We propose a method that improves the learning process by using additional data obtained from other experiments of the robot or even from experiments with different robot architectures. Incorporating experiences from other experiments requires transfer learning that has been used with success in machine learning. The proposed method can be used for arbitrary robot model, together with any type of learning algorithm. Experimental results indicate that task transfer between different robot architectures is a sound concept. Furthermore, clear improvement is gained on forward kinematics model learning in a task-space control task.