A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation.

Weiyong Si,Chenguang Yang,Ning Wang,Qinchuan Li

doi:10.3389/fnbot.2022.840240

Abstract

In this article, an impedance control-based framework for human-robot composite layup skill transfer was developed, and the human-in-the-loop mechanism was investigated to achieve human-robot skill transfer. Although there are some works on human-robot skill transfer, it is still difficult to transfer the manipulation skill to robots through teleoperation efficiently and intuitively. In this article, we developed an impedance-based control architecture of telemanipulation in task space for the human-robot skill transfer through teleoperation. This framework not only achieves human-robot skill transfer but also provides a solution to human-robot collaboration through teleoperation. The variable impedance control system enables the compliant interaction between the robot and the environment, smooth transition between different stages. Dynamic movement primitives based learning from demonstration (LfD) is employed to model the human manipulation skills, and the learned skill can be generalized to different tasks and environments, such as the different shapes of components and different orientations of components. The performance of the proposed approach is evaluated on a 7 DoF Franka Panda through the robot-assisted composite layup on different shapes and orientations of the components.

Highlights

Robots have been widely used in various fields, such as an industrial plant (Björnsson et al, 2018; Lamon et al, 2019; Rodrıguez et al, 2019; Raessa et al, 2020), medical healthcare (Tavakoli et al, 2020; Yang et al, 2020), rehabilitation exoskeleton (Li et al, 2019, 2020), space exploration (Papadopoulos et al, 2021) and it has great advantages on the repetitive accuracy and reducing the cost
A torque-computed framework based on impedance control was proposed to enable the human-robot skill transfer through teleoperation
The human user interface was developed to display the parameters of the controller and the contact force, and the human operator could modify the parameters of the control system

Summary

Introduction

Robots have been widely used in various fields, such as an industrial plant (Björnsson et al, 2018; Lamon et al, 2019; Rodrıguez et al, 2019; Raessa et al, 2020), medical healthcare (Tavakoli et al, 2020; Yang et al, 2020), rehabilitation exoskeleton (Li et al, 2019, 2020), space exploration (Papadopoulos et al, 2021) and it has great advantages on the repetitive accuracy and reducing the cost. These tasks often feature contactrich manipulation and significant uncertainty of the different tasks, such as variance among the products in flexible manufacturing. Our humans do not understand the principle behind manipulation, humans have the amazing capability to deal with the uncertainty and complexity in these tasks (Zeng et al, 2021). Roboticists proposed to make the robot learn the manipulation skills from humans. One of the main problems is how to learn complex and human-like manipulation skills. This study aims to develop a human-robot skill transfer system based on teleoperation and propose an approach to transfer human skills to robots.

Objectives

Methods

Conclusion