Automated Tuning of Non-Differentiable Rigid Body Simulation Models for Wheeled Mobile Robots

Simulation plays a crucial role in robotics development, yet creating and tuning accurate models with a small sim-to-real gap remains challenging and limits their broader applicability. This work introduces a pipeline for the automated tuning of models of wheeled mobile robots for simulation tools based on non-differentiable physics engines. Leveraging real-world motion data, the pipeline computes a sim-to-real error and applies black-box optimization to refine model parameters. Three design goals are followed: flexibility in implementing different optimization algorithms, applicability to wheeled mobile robots with diverse kinematics, and the ability to tune multiple model parameters. Experiments involving four wheeled mobile robots with differential, Ackermann, and omnidirectional kinematics validate the approach across diverse trajectories and algorithms. Results indicate successful automated tuning, revealing insights into the relationships between robot complexity, trajectory dynamics, and optimization algorithms.