Simulator-Based Digital Twin of a Robotics Laboratory

Simulator‑based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology‑independent model‑based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150‑hour undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low‑level reactive behaviors to high‑level deliberation—under a single formal model. A structured programming template ensures traceable, optimization‑free software synthesis, facilitating debugging and enabling self‑diagnosis of design flaws. The framework includes real‑time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and SLAM while avoiding dependency on specific middleware such as ROS~2. Results over three academic years indicate that students gain a deeper understanding of controller internals, and demonstrate improved ability to reason about system errors and their causes. The proposed environment thus offers an effective methodology for teaching end‑to‑end robot controller design through transparent, simulation‑driven digital twins.