Human-Adaptive Impedance Control Using Recurrent Neural Network for Human-Cooperative Robots

Abstract

This article proposes a human-adaptive impedance control to achieve human-robot co-manipulation, in which a recurrent neural network (RNN) estimates a human state that is used in improving the contact stability of impedance control. The human sate here is defined as what indicates that the human-arm stiffness becomes harder and the stability of the impedance control is deteriorating. In the proposed method, the human state is estimated from rectified-and-integrated electromyogram (iEMG) signals while the person is manipulating an object with a robot. According to the degree of the estimated human state, the proposed method changes the impedance parameters to be heavier online so as to make the system more stable and then returns the mechanical parameters to be lighter once the stability is restored. The validity of the proposed method is verified by experiments using a commercial-of-the-shelf manipulator. The experimental results showed that the proposed human-adaptive impedance control, which adjusts the impedance parameters online according to the human state, is effective to prevent the human-robot cooperative system from coming unstable.