Model Predictive Path Integral

Model Predictive Path Integral (MPPI) control is a sampling-based optimization technique used for planning and control in complex, dynamic systems, aiming to find optimal control sequences by simulating numerous trajectories and weighting them based on a cost function. Current research focuses on enhancing MPPI's efficiency and robustness through improved sampling strategies (e.g., using colored noise or learned distributions), incorporating safety guarantees via control barrier functions, and addressing challenges like local minima and high computational demands using techniques such as parallel processing on GPUs and hybrid approaches with gradient-based methods. This methodology holds significant promise for applications in robotics, autonomous driving, and other fields requiring real-time, safe, and optimal control in uncertain environments.