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

arXiv 2506.14855 ↗First posted 17 Jun 2025cs.RO cs.AIcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2017Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Drivingcited by 90
  2. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  3. 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
  4. 2022Predictive Sampling: Real-time Behaviour Synthesis with MuJoCocited by 47
  5. 2022Optimization-Based Control for Dynamic Legged Robotscited by 35
  6. 2019Feedback MPC for Torque-Controlled Legged Robotscited by 21
  7. 2023Automatic nonlinear MPC approximation with closed-loop guaranteescited by 12
  8. 2023TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollerscited by 10
  9. 2024Cafe-Mpc: A Cascaded-Fidelity Model Predictive Control Framework with Tuning-Free Whole-Body Controlcited by 10
  10. 2023MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPUcited by 7
  11. 2024Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retrainingcited by 7
  12. 2023Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentationcited by 6
4 more
  1. 2024Accelerating Model Predictive Control for Legged Robots through Distributed Optimizationcited by 6
  2. 2024Model Predictive Path Integral Control for Agile Unmanned Aerial Vehiclescited by 3
  3. 2024On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotioncited by 1
  4. 2025LP-MPPI: Low-Pass Filtering for Efficient Model Predictive Path Integral Controlcited by 1

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