Simulation-To-Reality Hyperparameter Optimization of MPPI Controllers via Bayesian Optimization in NVIDIA Omniverse Isaac Sim

Autonomous mobile robots navigating in dynamic environments require robust and efficient control strategies. Model Predictive Path Integral (MPPI) control offers flexibility and computational efficiency but relies on the careful tuning of multiple hyperparameters, which is traditionally performed through heuristic approaches. In this paper, we present an automated and systematic method for MPPI hyperparameter tuning using Bayesian Optimization (BO) within a ROS 2 and Nav2-based framework. By leveraging a high-fidelity digital twin implemented in NVIDIA Omniverse Isaac Sim, our method automates and accelerates hyperparameter tuning, significantly enhancing trajectory smoothness, reducing control effort, and improving overall navigation efficiency. Experimental validation on a real differential-drive robot demonstrates strong consistency between simulation-optimized parameters and real-world performance, confirming the effectiveness of our simulation-to-reality approach. This work provides a practical and reproducible method for integrating BO with ROS 2 and Nav2, enabling streamlined deployment and adaptive tuning of MPPI controllers in real-world robotic applications.