Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers
This addresses the problem of real-time control for complex robotic systems like quadrupeds and quadrotors, offering an incremental improvement over existing sampling-based MPC methods.
The paper tackles the computational limitations of Model Predictive Path Integral control for real-time robotic tasks by introducing Feedback-MPPI, which uses local linear feedback gains to enable rapid corrections without full re-optimization. Results show improved control performance and stability, allowing robust, high-frequency operation on a quadrupedal robot and a quadrotor.
Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks due to its flexibility in handling nonlinear dynamics and non-convex costs. However, its applicability in real-time, highfrequency robotic control scenarios is limited by computational demands. This paper introduces Feedback-MPPI (F-MPPI), a novel framework that augments standard MPPI by computing local linear feedback gains derived from sensitivity analysis inspired by Riccati-based feedback used in gradient-based MPC. These gains allow for rapid closed-loop corrections around the current state without requiring full re-optimization at each timestep. We demonstrate the effectiveness of F-MPPI through simulations and real-world experiments on two robotic platforms: a quadrupedal robot performing dynamic locomotion on uneven terrain and a quadrotor executing aggressive maneuvers with onboard computation. Results illustrate that incorporating local feedback significantly improves control performance and stability, enabling robust, high-frequency operation suitable for complex robotic systems.