Skill learning framework for human–robot interaction and manipulation tasks

Abstract

In this article, a learning framework that enables robotic arms to replicate new skills from human demonstration is proposed. The learning framework makes use of online human motion data acquired using wearable devices as an interactive interface for providing the anticipated motion to the robot in an efficient and user-friendly way. This approach offers human tutors the ability to control all joints of the robotic manipulator in real-time and able to achieve complex manipulation. The robotic manipulator is controlled remotely with our low-cost wearable devices for easy calibration and continuous motion mapping. We believe that our approach might lead to improving the human-robot skill learning, adaptability, and sensitivity of the proposed human-robot interaction for flexible task execution and thereby giving room for skill transfer and repeatability without complex coding skills. • This article presents a system that enables remote teleoperation based on wearable sensors for 6-Degree of freedom robotic manipulation control appication. • A skill learning framework is proposed based on dynamic movement primitives model from human manipulability to robot with the capability of replicating human demonstration.