Abstract

Collaborative robots provide prospective and great solutions to human–robot cooperative tasks. In this paper, we present a comprehensive review for two significant topics in human–robot interaction: robots learning from demonstrations and human comfort. The collaboration quality between the human and the robot has been improved largely by taking advantage of robots learning from demonstrations. Human teaching and robot learning approaches with their corresponding applications are investigated in this review. We also discuss several important issues that need to be paid attention to and addressed in the human–robot teaching–learning process. After that, the factors that may affect human comfort in human–robot interaction are described and discussed. Moreover, the measures utilized to improve human acceptance of robots and human comfort in human–robot interaction are also presented and discussed.

Highlights

  • Collaborative robots provide prospective and great solutions to complex hybrid assembly tasks, especially in smart manufacturing contexts [1,2]

  • The issue of correspondence is very important in human–robot interaction, where it refers to the identification of a mapping between the human teacher and the robot learner that allows for the transfer of information in the human–robot team [9]

  • We have presented and discussed two significant topics in human–robot interaction: learning and comfort

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Summary

Introduction

Collaborative robots provide prospective and great solutions to complex hybrid assembly tasks, especially in smart manufacturing contexts [1,2]. In order to shorten this gap between research and application, one of the up-to-the-moment topics—robot learning from human demonstrations—is proposed and has been studied by both academia and industry in recent years [6,7] Via this approach, humans can transfer knowledge to robots through demonstrated actions without needing considerable coding skills to have robots understand how to accomplish tasks [8,9]. 2 provides and the comfort—in topic of teaching and learning human–robot interaction, which significant topics—learning this review. Section 2discussion provides the topic of teaching and learning in human–robot interaction, whichand contains learning approaches. The conceptual details and corresponding applications of each sub-topic are discussion of robots learning from demonstration, human teaching approaches, and robot learning presented.

Section
Robot Learning from Demonstration
Kinesthetic-Based Teaching
Joystick-Based Teaching
Immersive Teleoperation Scenarios Teaching
Wearable-Sensor-Based Teaching
Natural-Language-Based Teaching
Vision-Based Teaching
Kinesthetic-Based Learning
One-Shot Learning
Multi Shot Learning
Vision-Based Learning
Reinforcement-Learning-Based Approach
Inverse-Reinforcement-Learning-Based Approach
Skill-Tree-Construction-Based Approach
Syntactics-Based Approach
Semantic-Networks-Based Learning
2.3.10. Neural-Models-Based Learning
2.3.11. Procedural-Memory-Based Learning
Extraction
Real-Time
Correspondence
Execution
Safety
What Affects Human Comfort in Human–Robot Interaction?
Robot Response Speed
Human–Robot
Robot Object-Manipulating Fluency
Human Coding Efforts
Robot Sociability
Factors Outside Human–Robot Teams
Human Comfort Measurement
Self-Evaluation Approach
Physiological Approach
Measures to Improve Human Acceptance of Robots
Measures to Improve Human Comfort
Conclusions
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