skill learning, interaction, teleoperation, dynamical movement primitive
With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed of the motors. We present a rotation-invariant dynamical-movement-primitive method for learning interaction skills. We also conducted robotic teleoperation demonstrations and designed imitation learning experiments. The experimental human-robot interaction results confirm the effectiveness of the proposed method.
Tsinghua University Press
Bin Fang, Xiang Wei, Fuchun Sun et al. Skill Learning for Human-Robot Interaction Using Wearable Device. Tsinghua Science and Technology 2019, 24(06): 654-662.