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

TL;DR

Re4MPC is a reactive motion planning approach that combines nonlinear model predictive control with deep reinforcement learning to efficiently generate trajectories for complex robots, outperforming traditional methods in success rate and computation time.

Contribution

This paper introduces Re4MPC, a novel framework integrating NMPC with DRL for reactive, multi-model motion planning in high-DOF robots, enhancing efficiency and success.

Findings

Re4MPC is more computationally efficient than baseline NMPC.

Re4MPC achieves higher success rates in reaching goals.

The framework effectively adapts model selection based on task complexity.

Abstract

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.