A Robot Human-Like Learning Framework Applied to Unknown Environment Interaction

Xianfa Xue,Lei Zuo,Ning Wang,Atila Bueno

doi:10.1155/2022/5648826

Abstract

Learning from demonstration (LfD) is one of the promising approaches for fast robot programming. Most learning systems learn both movements and stiffness profiles from human demonstrations. However, they rarely consider the unknown environment interaction. In this paper, a robot human-like learning framework is proposed, where it can learn human skills through demonstration and complete the interaction task with an unknown environment. Firstly, the desired trajectory was generated by dynamic movement primitive (DMP) based on human demonstration. Then, an adaptive optimal admittance control scheme was employed to interact with environments with the reference adaptation method. Finally, the experimental study was conducted, and the effectiveness of the framework proposed in this paper was verified via a group of curved surface wiping experiments on a balloon with unknown model parameters.

Highlights

Robot learning from demonstration (LfD) has recently drawn much attention because of its high efficiency in robot programming [1]. us, robots can quickly program the robots to perform operating variable skills and replace human tutors from such tasks in a complex industrial environment [2]
In the proposed learning framework, the human tutor presents a demonstration at first. e trajectory learned from the Dynamic movement primitive (DMP) model is regarded as the desired trajectory. en, the desired trajectory and the interaction force measured by the force sensor are input into an adaptive admittance controller to obtain the modified reference trajectory
A robot human-like learning framework based on robot and unknown environment interaction was proposed. e LfD approach can make the robot obtain the reference input more quickly and accurately

Summary

Introduction

Robot learning from demonstration (LfD) has recently drawn much attention because of its high efficiency in robot programming [1]. us, robots can quickly program the robots to perform operating variable skills and replace human tutors from such tasks in a complex industrial environment [2]. Robot learning from demonstration (LfD) has recently drawn much attention because of its high efficiency in robot programming [1]. Us, robots can quickly program the robots to perform operating variable skills and replace human tutors from such tasks in a complex industrial environment [2]. How to use the information of the human tutor is very important. Dynamic movement primitive (DMP) is a kind of common method in humanrobot skill transfer tasks [5]. We can use regression algorithm to quickly learn model parameters in the online trajectory planning of robots [6]. The DMP model is easy to generalize; we can quickly generalize a trajectory with the same style as the original trajectory by adjusting the starting and ending coordinates of the trajectory [7, 8]. Because of the above advantages, the DMP has been widely used in humanrobot skill transfer tasks [9]

Methods

Results

Conclusion