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
• Authors: Tommaso Belvedere, Michael Ziegltrum, Giulio Turrisi, Valerio Modugno,
• 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.

Related papers

• Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning [0.94371657253557]
  We propose a novel multi-model motion planning pipeline termed Re4MPC.<n>It computes trajectories in reaching end-effector goals using Model Predictive Control.<n>We show that Re4MPC is more computationally efficient and achieves higher success rates than the NMPC baseline.
  arXiv  Detail & Related papers  (2025-06-10T01:58:32Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [50.34179054785646]
  We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.<n>Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development.
  arXiv  Detail & Related papers  (2025-04-17T12:57:11Z)
• Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing [11.313274230727549]
  Motion Cueing Algorithms encode the movement of simulated vehicles into movement that can be reproduced with a motion simulator.<n>This paper introduces a novel learning-based MCA for serial robot-based motion simulators.<n>By shifting the computational burden to offline training, the new algorithm enables real-time operation at high control rates.
  arXiv  Detail & Related papers  (2025-04-01T06:52:30Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics [14.149584412213269]
  We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. To address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed.
  arXiv  Detail & Related papers  (2024-03-23T04:36:12Z)
• Learning Control from Raw Position Measurements [13.79048931313603]
  We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO. It is specifically designed for application to mechanical systems where velocities cannot be directly measured.
  arXiv  Detail & Related papers  (2023-01-30T18:50:37Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.