Implicit and Intuitive Grasp Posture Control for Wearable Robotic Fingers: A Data-Driven Method Using Partial Least Squares

Abstract

Functionality of a human hand can be augmented with wearable robotic fingers to enable grasping and manipulation of objects with a single hand. Such technology will have applications in manufacturing and construction, as well as health care. This paper presents a method for controlling extra robotic fingers, termed “Supernumerary Robotic Fingers (SR Fingers),” in coordination with human fingers to grasp diverse objects. Two hypotheses are proposed and verified through experiments. One is that humans prefer grasp posture of their fingers and that of the SR Fingers to be highly correlated when working together, which is represented with a few principal components, resembling grasp synergy in neuromotor control. The other hypothesis is that SR Finger posture can be controlled to coordinate with human finger posture via grasp synergy of the hybrid human–robotic hand. Partial least squares regression is used for predicting a desired posture of the SR Fingers from the measurement of human fingers. This method is implemented on a pair of wrist-mounted SR Fingers. Experiments demonstrate that the prototype SR Fingers can assist the human user in performing single-handed grasping tasks without requiring explicit commands.