Learning to Assist Bimanual Teleoperation Using Interval Type-2 Polynomial Fuzzy Inference

Abstract

Assisting humans in collaborative tasks is a promising application for robots, however effective assistance remains challenging. In this article, we propose a method for providing intuitive robotic assistance based on learning from human natural limb coordination. To encode coupling between multiple-limb motions, we use a novel interval type-2 (IT2) polynomial fuzzy inference for modeling trajectory adaptation. The associated polynomial coefficients are estimated using a modified recursive least square with a dynamic forgetting factor. We propose to employ a Gaussian process to produce robust human motion predictions, and thus address the uncertainty and measurement noise of the system caused by interactive environments. Experimental results on two types of interaction tasks demonstrate the effectiveness of this approach, which achieves high accuracy in predicting assistive limb motion and enables humans to perform bimanual tasks using only one limb.