Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer

Abstract

It has been established that the transfer of human adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation skills. In this paper, we propose a novel interface for human impedance adaptive skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.