## 1 Introduction  
Collaborative robotic applications are, nowadays, well-established and commonly used concepts of technology. The main goal of such applications is to combine the strengths of both humans and robotic systems to achieve maximum effectiveness in completing a specified task while minimizing the risks imposed on the human worker. In the manufacturing process, these applications are crucial in enabling the concept referred to as \"Industry 4.0\". Industry 4.0 focuses on developing so-called cyber-physical systems or CPS for short, aiming to create highly flexible production systems capable of fast and easy changes, addressing the need for individualized mass production of the current markets [1]. Collaborative applications are becoming increasingly popular on the factory floors due to their various benefits, such as lower deployment costs, compact sizes, and easier repurposing, than standard robotic systems [2]. As a result, more factory workers will be required to work in close contact with robotic systems. This, however, introduces new arduous challenges to overcome, primarily how to secure the safety of a human worker, while ensuring high work effectiveness. The former problem focuses mainly on minimizing potential risks and avoiding accidents from collaborative work between the human worker and a robot, not considering the work efficiency. The latter aims to find the methods capable of maximizing the overall productivity of such collaboration. Albeit the safety of the human worker is an absolute priority, also being the most currently researched topic [3], the economic benchmarks are vital in determining further deployment of these applications. Thus, it is necessary to pay more attention to the interaction between the human and robotic systems, also referred to as HRI, researching the impact and influence of the whole human-robot relationship, developing new approaches capable of meeting today's production demands. In today's conventional robotic application, usually involving robust industrial manipulators, the robot works in a cell that must be wholly separated from the workers' environment by a physical barrier [4]. The human only comes in contact with the robot during maintenance and programming, carried out by highly qualified personnel. All of this is not only expensive, but also time-consuming. Alternatively, in collaborative applications, factory workers may often get into contact with parts of the robot and are even encouraged to influence the robot's actions based on the current circumstances to secure maximum work flexibility. This could pose a severe problem, where technically unskilled workers may suffer great difficulties communicating with the robotic system or conducting some form of control. A solution to this problem lies in developing such HRI methods, which would enable natural and ergonomic human-robot communication without the need for prior technical knowledge of the robotic systems. Hence, the workers could solely focus on the task at hand and not waste time figuring out the details of the application's interface. For humans, the most natural way of communication is through speech, body, or facial gestures. Implementing these types of interfaces into collaborative applications could yield many benefits, which lead to increased productivity and overall worker contentment [5]. This paper presents a gesture-based collaborative framework for controlling a robotic manipulator with flexible potential deployment in the manufacturing process, academic field, or even a commercial sector. The underlying basis of the proposed framework is based on the previous work of [PERSON] et al. [6], which introduced the concept of \"linear HRI,\" initially designed for mobile robotic applications.This paper further develops this concept, widening its usability from mobile robotic applications to robotic arm manipulation while utilizing a new state-of-the-art visual-based sensory system.