Independent Joint Learning: A Novel Task-to-Task Transfer Learning Scheme for Robot Models

Abstract

In the past decade, model learning techniques have provided appealing approaches for determining the dynamic model of robots from data. These techniques strongly capture the complicated effects of robot dynamics, which are often neglected in hand-crafted dynamic models. However, unlike robust performance shown in trained tasks, learned models do not exhibit a reliable performance in new tasks as they are valid only near the domain of the trained tasks. In this paper, we propose an alternative approach for task-to-task transfer learning, called "Independent Joint Learning (IJL)." IJL learns the model for each joint independently rather than the whole body at one time to effectively transfer knowledge between tasks. A comparative simulation study on a 6 DOF PUMA robot demonstrates that our approach outperforms other related approaches when a task different from trained tasks is proposed.