用深度强化学习让机器人实时选择最优运动规划模型，提升效率与成功率。
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
- 基于DRL动态选择NMPC的模型、代价和约束，实现智能响应
- 在仿真中比传统方法快且成功率更高，末端达目标率显著提升
- 适合高自由度机器人实时运动规划，尤其适用于复杂任务场景
针对高自由度机器人（如移动操作臂）的传统运动规划方法在真实环境中计算成本过高问题，本文提出一种新型多模型运动规划框架Re4MPC，基于非线性模型预测控制（NMPC）生成轨迹。Re4MPC通过深度强化学习（DRL）框架学习反应式决策策略，根据任务复杂度与机器人状态动态选择NMPC的模型、代价函数和约束条件，从而实现高效轨迹计算。文中提出数学形式将NMPC融入DRL框架。在基于物理引擎的移动操作臂仿真中验证了该方法，结果表明：相比不采用学习机制的全系统NMPC基线，Re4MPC在计算效率上更优，且在达成末端执行器目标方面成功率更高。
原文摘要 · Abstract (English)
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.
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