Re4MPC：通过深度强化学习实现多模型运动规划的反应式非线性 MPC
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
AI总结：
本文提出 Re4MPC，用深度强化学习根据任务和状态反应式选择 NMPC 的模型、代价与约束，在移动操作机器人仿真中提升了计算效率和末端执行器目标到达成功率。
摘要：
针对移动操作机器人等具有多自由度机器人的传统运动规划方法，在真实环境中往往计算成本过高。本文提出一种名为 Re4MPC 的新型多模型运动规划流水线，利用非线性模型预测控制（NMPC）计算轨迹。Re4MPC 根据任务复杂度和机器人状态，反应式地选择 NMPC 问题的模型、代价与约束，从而以计算高效的方式生成轨迹。这种反应式决策策略通过深度强化学习（DRL）框架学习得到。文中提出了一种将 NMPC 集成到该 DRL 框架中的数学表述。为验证方法和设计选择，作者在涉及移动操作机器人的物理仿真中评估了 DRL 训练与测试结果。实验结果表明，与没有该学习机制、计算全身轨迹的 NMPC 基线相比，Re4MPC 计算效率更高，并且在到达末端执行器目标方面取得了更高的成功率。
英文摘要：
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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