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

• URL: http://arxiv.org/abs/2506.14855v1
• Date: Tue, 17 Jun 2025 07:47:33 GMT
• Title: Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers
• Abstract summary: Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks.<n>This paper introduces robust feedback gains derived from sensitivity used in gradient-based MPC.<n>We demonstrate the effectiveness of F-MPPI in simulations through real-world experiments on two robotic platforms.
• Score: 0.9674641730446749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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