# Design of Advanced Human-Robot Collaborative Cells for Personalized Human-Robot Collaborations
## 1 Introduction  
Human-robot collaboration (HRC) is expected to be a core element of future factories. Combining the repeatability and intelessness of robots with humans' versatility and problem-solving skills often boosts the flexibility and productivity of industrial processes. However, the design of effective methodologies to steer the deployment of this new paradigm in real production environments is an open challenge for both researchers and companies [1]. According to [2], work organization and technical solutions for Cyber-Physical Systems (CPS) are supposed to evolve between two extreme alternatives: (i) the _techno-centric scenario_ and; (ii) the _anthropo-centric scenario_. In the techno-centric scenario, the technological aspects dominate the organization of the work. In contrast, in the anthropo-centric scenario, human workers control the work, and technology helps them make decisions. Existing approaches to CPS (and, among them, HRC) oscillate between these two extremes. However, human factors have gained more attention in the design of novelmethodologies for personalized production dynamics based on the operators' preferences, technical skills, and health-related issues. Regarding HRC, human factors have been considered at different levels [3]. For example, optimization of human factors have been embedded into a task scheduler [4]; task allocation have been exploited to reduce the workload of human workers [5; 6]; task synergy between human-robot tasks were optimized to reduce the cycle time [7]; human-aware motion planners demonstrated to be preferable by human users [8]. The works mentioned above mainly focus on the planning aspects of HRC. However, they disregard the complex effect of human-robot communication on the user experience. A stuttering human-system communication is often a major bottleneck to a fruitful collaborative process. For this reason, the communication between the human operator and the system is an object of intense study. In this regard, Augmented Reality (AR) is a striking tool able to overlay instructions and knowledge from CPSs to the physical operator's view [9]. Driven by this consideration, an EU-funded research project called SHAREWORK (EU Horizon 2020--[[https://sharework-project.eu](https://sharework-project.eu)]([https://sharework-project.eu](https://sharework-project.eu)) (accessed on 21 June 2022)) aims to provide an all-around approach to HRC, where robots and operators collaborate at different cognitive and physical levels. A key objective of SHAREWORK is to make implicit and explicit communications between robots and humans smooth and fruitful. Explicit communications leverage multi-modal technologies and, in particular, Augmented Reality tools. Implicit communications require the robotic system to reason on the operator's intentions and act consequently. Therefore, task representation and planning are fundamental to provide the robot with the necessary autonomy and suitable initiative. This paper presents some of the results of the SHAREWORK project. In particular, it presents the design methodology and deployment actions needed to provide a _user-aware_ approach to HRC that enhances the flexibility of HRC systems. In addition, human-aware paradigms usually consider a one-fits-all solution, considering the human an anonymous agent. Here, we go beyond this concept and propose a user-centric methodology to shape the robots' behavior based on the specific characteristics of a single user (e.g., age, skills, experience) and preferences (e.g., left-handed vs. right-handed) [10], i.e., implementing personalized robot behavior that can better serve the human operator and, potentially, increase the technology perception and acceptance. Therefore, we propose the integration of planning, perception and communication into a unified technological framework.