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Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning

arXiv 2506.08344 ↗First posted 10 Jun 2025cs.RO cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this paper, we propose a novel multi-model motion planning pipeline, termed Re4MPC, which computes trajectories using Nonlinear Model Predictive Control (NMPC). Re4MPC generates trajectories in a computationally efficient manner by reactively selecting the model, cost, and constraints of the NMPC problem depending on the complexity of the task and robot state. The policy for this reactive decision-making is learned via a Deep Reinforcement Learning (DRL) framework. We introduce a mathematical formulation to integrate NMPC into this DRL framework. To validate our methodology and design choices, we evaluate DRL training and test outcomes in a physics-based simulation involving a mobile manipulator. Experimental results demonstrate that Re4MPC is more computationally efficient and achieves higher success rates in reaching end-effector goals than the NMPC baseline, which computes whole-body trajectories without our learning mechanism.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 36 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

18 of 36 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2021iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Taskscited by 194
  4. 2019Data Efficient Reinforcement Learning for Legged Robotscited by 63
  5. 2019HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulatorscited by 55
  6. 2021A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulationcited by 38
  7. 2020Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environmentscited by 23
  8. 2021A Holistic Approach to Reactive Mobile Manipulationcited by 17
  9. 2021A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architecturescited by 12
  10. 2022CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimalitycited by 12
  11. 2021Multi-Modal Model Predictive Control through Batch Non-Holonomic Trajectory Optimization: Application to Highway Drivingcited by 9
  12. 2015Relaxed Logarithmic Barrier Function Based Model Predictive Control of Linear Systemscited by 8
6 more
  1. 2021Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitivescited by 8
  2. 2022Deep Reinforcement Learning based Robot Navigation in Dynamic Environments using Occupancy Values of Motion Primitivescited by 6
  3. 2021Constraint Handling in Continuous-Time DDP-Based Model Predictive Controlcited by 4
  4. 2022An Architecture for Reactive Mobile Manipulation On-The-Movecited by 3
  5. 2022Adaptive Complexity Model Predictive Controlcited by 1
  6. 2023Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planningcited by 1

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  2. 2024DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation
  3. 2025Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control
  4. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  5. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
  6. 2025URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning
  7. 2026Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation
  8. 2025A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning