Learning Contact Tasks Skills Based on DMP and Affordance Templates

Abstract

Learning from demonstration (LfD) enables robots to learn experts’ skills by human demonstration. Recently, LfD has been developed for learning and performing skills in contact-rich tasks. However, task performance has not been generalized to unknown poses in contact-rich tasks. In this paper, we propose a teleoperation-based learning from demonstration (LfD) framework for performing contact-rich tasks in unknown poses. Expert demonstrations are collected via a bilateral teleoperation system, with an orientation synchronization algorithm aiding intuitive manipulation. From demonstrations, position and wrench profiles are recorded. Task trajectories are learned using dynamic movement primitives (DMP), while strategy learning allocates input and compliance spaces based on affordance templates to adapt motion during contact. By combining trajectory and strategy learning, the framework successfully reproduces manipulation behaviors in novel configurations. Experiments on turning-valve and peg-in-hole insertion validate the method, showing improved success rates and robustness to pose variations.