# Facilitating Robot Learning in Virtual Environments: A Deep Reinforcement Learning Framework
## 4 Process of Digital Twin Creation  
The concept of a digital twin has become increasingly prominent, particularly in the field of industrial automation. Figure 1 illustrates the digital twin of the UR5e cobot, which was developed as part of this research project. In this section, we outline a generalized process and method developed during the study. This process is intended to guide both companies and researchers in creating digital twins for robots that require testing using reinforcement learning techniques. The creation of a digital twin begins with designing an RL-compatible environment, following standard APIs like OpenAI Gym to ensure compatibility with RL libraries such as Stable-Baselines3 and TensorFlow Agents [29]. The RL agent perceives the environment through simulated sensor data, selects actions from a predefined action space, and receives rewards based on task performance. The reward structure is designed to align with training objectives, guiding the agent toward optimal behavior. To enhance real-world applicability, domain randomization introduces variations in textures, lighting, and physical parameters, improving generalization. After training, learned policies are validated in simulation and refined for real-world deployment. This approach accelerates robotic development while reducing costs and risks. In this study, Webots is integrated with Stable-Baselines3 to provide a structured RL testing environment. Stable-Baselines3 offers scalable RL algorithms, facilitating efficient communication between the agent and simulation. This integration supports iterative optimization, task design, and real-world readiness. Figure 4 (UML sequence diagram) presents the workflow for developing and deploying a digital twin in a production environment, involving key roles such as the Production Company, Simulation Engineer, and RL Engineer. The following section details the steps of this process. 1. Defining Requirements for the Digital Twin: The Production Company first defines the digital twin's requirements, which are communicated to the Simulation Engineer. This includes specifying the robot's tasks (e.g., pick-and-place, welding), operational constraints (speed, payload, workspace limits), and necessary precision. Key data requirements for reinforcement learning include CAD models, sensor data, and historical operational data. Data fusion integrates information from sources like robot controllers and industrial protocols. Effective data management ensures accuracy and reliability, enabling the digital twin to replicate real-world conditions for training and validation. The following Table 1 summarizes these data requirements. 2. Creating the Detailed Robot and Environmental Model in Webots: The Simulation Engineer develops a detailed robot and environment model within Webots. The robot model includes kinematics, dynamics, actuators, sensors, and an end-effector, ensuring accurate simulation of motion and interactions. The environment model replicates the production layout, including objects, obstacles, and sensor placements. Balancing model fidelity and computational efficiency is essential. While high-detail models improve realism, they increase computational cost. The appropriate level of detail depends on task complexity, ensuring effective reinforcement learning training without unnecessary resource overhead. 3. Simulation and Performance Bottleneck Identification: Once the digital twin is created in Webots, the Production Company runs simulations to test different layouts, optimize workflows, and identify bottlenecks. By analyzing robot performance under varying conditions, such as speed, load, and potential failure scenarios, engineers can detect inefficiencies, reachability issues, and collision risks before physical deployment. Simulation data, including cycle times, trajectory analysis, and energy consumption, provide insights for refining system design and improving operational efficiency. This virtual testing approach reduces costs and minimizes disruptions to the production process. 4.