# Learning and Comfort in Human-Robot Interaction: A Review
## 1 Introduction  
Collaborative robots provide prospective and great solutions to complex hybrid assembly tasks, especially in smart manufacturing contexts [1; 2]. Through human-robot interaction, the tasks can be split between humans and robots based upon their capabilities to leverage their unique advantages. For example, in human-robot collaborative assembly tasks, robots can execute tedious and strength-based sub-tasks, while humans can conduct brainwork-based sub-tasks [3; 4]. In general, collaborative robots are mainly programmed and controlled by well-trained experts through offline coding devices, such as teaching pendants [5], which usually cost humans significant time and effort. However, modern smart manufacturing is experiencing quick product upgrades with more customization and shorter life cycles to meet the ever-changing market needs. 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].  
Apart from the understanding of human actions and intentions in human-robot interaction [10; 11], human comfort, which has a direct and immediate influence on the collaboration quality between the robot and its human partner, is also a significant factor for the robot to be aware of [12]. For instance, in human-robot collaborative tasks, technical safety (keeping a required physical distance or developing a safety interlock system between the human and the robot) does not necessarily mean human perceived safety and comfort since the perceived feelings are mostly subjective [13]. In addition, the same performance of the robot in the same task may lead to different humans having diverse comfort levels.  
For example, a slow robot speed usually makes some people feel safe in a human-robot interaction. However, some others may consider it to be less effective and feel uncomfortable.  
Therefore, it is in the human-robot interaction context that we will introduce and discuss two significant topics--learning and comfort--in this review. The rest of the review is organized as follows. Section 2 provides the topic of teaching and learning in human-robot interaction, which contains discussion of robots learning from demonstration, human teaching approaches, and robot learning approaches. The conceptual details and corresponding applications of each sub-topic are presented. Several important issues, including extraction, real-time, correspondence, execution, and safety in the human-robot teaching-learning process are discussed in Section 3. Section 4 describes the details about what affects human comfort in human-robot interaction. Different kinds of factors that may influence human comfort are investigated and discussed with related studies. Section 5 describes how to improve human comfort in human-robot interaction, where the three sub-topics of human comfort measurement, measures to improve human acceptance of robots, and measures to improve human comfort are included and discussed. Finally, conclusions are presented in Section 6.