In-air Knotting of Rope with Dual-Arm two-Fingers Robot using Deep Predictive Learning

Abstract

In this study, we realized in-air knotting of rope with the dual-arm two-fingers robot by using deep predictive learning. By training two deep neural networks (CAE and LSTM) with robots’ sensorimotor experiences including visual and proximity sensors, we had the robot perform bowknot and overhand knot while dynamically responding to the state of the rope online. In addition, since we designed task motion based on the Ian knot method by using the dual-arm two-fingers robot, our method does not require a dedicated workbench or robot hand. As a result of the experiments, it was confirmed that the robot could perform tasks with high accuracy even for the unlearned rope.