# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 1 Introduction
### Analysis of Past Reviews  
In the last few years, several review articles on HRC have appeared that provide a summary of previous research and a comprehensive view of this wide research field. This section provides a short analysis of past review articles in an attempt to clarify the differences in the structure and type of selected information, as well as in the depth to which the topics are covered. [PERSON] et al. [2] reviewed the state-of-the-art on technologies and methodologies enabling achieving advanced human-robot collaborative systems. That paper describes in a very complete way the bi-directional human-robot interfaces and interaction modalities, as well as adaptive control methodologies to link perception to robot behaviour, namely for setting up a successful physical HRC (pHRC) framework. Additionally, the authors discuss the challenges of control performance in terms of stability and transparency, including useful lessons from the field of human motor control. Potential applications and relevant use cases are also presented, jointly with benchmarks regarding acceptance aspects. [PERSON] et al. [5] provide an extensive review of HRC for industrial environments, focusing on problems related to physical and cognitive interaction. Safety standards are presented from the perspective of requirements and design guidelines useful for development of new systems. The main body of the paper is on state-of-the-art approaches for developing intuitive user interfaces, including interaction modes and robot programming in manufacturing practice. The main industrial applications where collaborative robots may improve task efficiency are also discussed. In [4], [PERSON] et al. address the programming requirements for effective collaboration in industrial settings. The review is organized around three important aspects: communication, optimization, and learning. Communication between humans and robots is categorised in body language and speech, user interfaces and haptics. The optimization approach is centred on of different aspects related to the comfort of the human operator and task efficiency. Research studies exploring learning to allow generation of coordinated actions are also discussed, with emphasis on learning from demonstration and reinforcement learning techniques. Other review articles are organized in a chronological perspective that helps to analyse the trends observed in recent years. [PERSON] et al. [6] conducted a literature review of collaborative robotics in manufacturing or assembly applications, covering the period from 2009 to 2018. The selected literature comprises a total of 35 case studies where practical experiments with real hardware are conducted. The analysis of the reviewed literature summarizes each individual study, providing information such as the robot used, the control system, the collaboration methodology, the industrial application, as well as the objective in HRC research and the key findings. [PERSON] et al. [3] provide a broad coverage of the state-of-the-art on industrial collaborative robotics by reviewing more than 300 papers published between 2008 and 2017. For that purpose, authors classify the content of these papers into seven research categories, as follows: \"hardware and software design of robotic systems\", \"safety in industrial robotics\", \"cognitive human-robot interactions\", \"robot programming approaches\", \"human-robot task allocation\", \"virtual and augmented reality\", \"study of physical interactions between humans\" and \"fault tolerance\". In addition to this classification, each selected article is grouped in several sub-categories, while the proposed approach, and the main results are summarized. In a recent review, [PERSON] et al. [7] give an overview of Human-Robot Collaboration in industrial environments centred around three challenges facing the development of collaborative systems: human safety, trust in automation (i.e., user's expectations and acceptance) and productivity impact.