Low-pass sampling in Model Predictive Path Integral Control

Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, offering flexibility in handling arbitrary dynamics and cost functions. However, the original MPPI suffers from high-frequency noise in the sampled control trajectories, leading to actuator wear and inefficient exploration. In this work, we introduce Low-Pass Model Predictive Path Integral Control (LP-MPPI), which integrates low-pass filtering into the sampling process to eliminate detrimental high-frequency components and improve the effectiveness of the control trajectories exploration. Unlike prior approaches, LP-MPPI provides direct and interpretable control over the frequency spectrum of sampled trajectories, enhancing sampling efficiency and control smoothness. Through extensive evaluations in Gymnasium environments, simulated quadruped locomotion, and real-world F1TENTH autonomous racing, we demonstrate that LP-MPPI consistently outperforms state-of-the-art MPPI variants, achieving significant performance improvements while reducing control signal chattering.