Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers

TL;DR

Feedback-MPPI enhances sampling-based model predictive control by integrating local feedback gains derived from sensitivity analysis, enabling real-time, high-frequency robotic control with improved stability and robustness.

Contribution

The paper introduces Feedback-MPPI, a novel framework that combines MPPI with local feedback gains from sensitivity analysis for faster, more stable control.

Findings

Improved control performance and stability in robotic experiments.

Enables high-frequency control suitable for complex tasks.

Effective in both simulation and real-world robotic platforms.

Abstract

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.