## 3 Materials and Methods
Algorithms like YOLACT and the latest versions of YOLO enable image segmentation, which can be utilized to calculate the orientation and size of detected objects on the work surface. Furthermore, the algorithm's output can be used to determine the robot's pick-up positions. The workplace setup consists of a desk and two cameras: one focused on the desk for object detection and the other dedicated to capturing the operator's gestures. A comparable setup was developed by the authors in [49], who utilized two collaborative robotic arms in their design. The layout of the robotic workspace, as modeled in the ABB RobotStudio simulation software (ver. 2021), is depicted in Figure 2[50]. In the proposed configuration, the collaborative robot chosen is the dual-arm ABB Yumi (IRB 14000, ABB Inc. Robotics & Discrete Automation, Auburn Hills, MI, USA). The dimensions of the desk and the overall workspace are designed for easy relocation and to optimize space usage in the laboratory, as the tool-feeding application does not require extensive space. Designing a robotic assistant for tool dispensing involves a complex solution that must be deconstructed into manageable components, which can be addressed individually before being integrated into a cohesive system. The design of the robotic workplace encompasses the following key elements:  
1. Detection of work tools on the worktable. 2. Recognition of hand gestures. 3. Integration of a collaborative robot and cameras within the robotic workplace. Figure 2: Design of a robotic workplace in ABB RobotStudio. After evaluating various factors, it was determined that selecting different types of hand tools for the workbench would be the most practical solution. The primary reason for this choice is the practicality and usability of a robotic hand tool-feeding application in real-world scenarios, particularly in production environments. Robotic feeding of hand tools to human operators can be especially useful on assembly lines, where it can help improve productivity by minimizing the time operators spend reaching or bending for tools. Since a preexisting dataset of hand tools that met specific requirements could not be found, a custom dataset was created using the myGym tool [51]. The Generate Dataset tool within myGym allows for the creation of a synthetic dataset tailored for the custom training of vision models. A range of commonly used hand tools was selected, including combination pliers, a hammer, a flat screwdriver, a Phillips screwdriver, and various sizes of open-end wrenches (Figure 3). Each type of tool presents unique characteristics, shapes, purposes, and quantities, which increases the complexity of the robotic tool-feeding application. During the creation of the dataset, several key factors were varied, including the textures and colors of the objects, lighting conditions, and tool models. The dataset was generated multiple times with different color schemes for the tools and varying the number of images, significantly increasing the diversity and variability of the training data. This approach allowed for the simulation of a wide range of real-world visual scenarios, improving the model's robustness and ability to recognize tools under diverse conditions. The variability in lighting and tool appearance was specifically aimed at enhancing the model's performance in environments with fluctuating lighting, varying tool positions, and different background settings. Although the synthetic dataset provides a controlled environment for training, further testing in real-world conditions is necessary to fully assess the model's applicability and robustness. Finally, the ABB Yumi robot was utilized, mounted on a custom-made aluminum base. The initial setup of the workplace required several modifications. The first iteration involved mounting a camera directly on the robot, as depicted in Figure 4 left. However, this arrangement proved unstable. In the second version, both cameras were mounted on a stand positioned between the robot's arms, as shown in Figure 4 middle.