Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
This addresses motion planning challenges for robots like mobile manipulators, offering an incremental improvement by integrating DRL with NMPC for better efficiency.
The paper tackles the computational inefficiency of traditional motion planning for robots with many degrees-of-freedom by proposing Re4MPC, a multi-model pipeline that uses Nonlinear Model Predictive Control (NMPC) with reactive decision-making learned via Deep Reinforcement Learning (DRL), resulting in higher computational efficiency and success rates compared to a baseline NMPC method.
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.