用扩散模型指导力反馈MPC，实现复杂工况下的精准去毛刺。
Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
- 用扩散模型提供运动先验，提升MPC在不同任务中的适应性
- 实测在难达位置仍能稳定保持法向力并完成圆周去毛刺
- 首次将扩散模型与力反馈MPC结合用于有碰撞约束的工业去毛刺
模型预测控制（MPC）广泛应用于扭矩控制机器人，但传统方法常忽略实时力反馈，在存在碰撞约束的接触密集型工业任务中表现不佳。去毛刺任务尤其需要精确的工具插入、稳定的力调控以及在复杂构型下的无碰撞圆周运动，超出标准MPC的能力范围。本文提出一种融合力反馈MPC与基于扩散模型的运动先验的框架。扩散模型作为运动策略的记忆库，为多个任务实例提供鲁棒的初始化与自适应能力；MPC则确保执行过程的安全性，具备显式的力跟踪、扭矩可行性及碰撞规避能力。我们在一台扭矩控制机械臂上验证了该方法在工业去毛刺任务中的有效性。实验表明，即使在难以到达的构型和障碍物约束下，仍能实现可靠的工具插入、精确的法向力跟踪以及稳定的圆周去毛刺运动。据我们所知，这是首个将扩散运动先验与力反馈MPC结合用于碰撞感知、接触密集型工业任务的研究。
原文摘要 · Abstract (English)
Model Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation, and collision-free circular motions in challenging configurations, which exceeds the capability of standard MPC pipelines. We propose a framework that integrates force-feedback MPC with diffusion-based motion priors to address these challenges. The diffusion model serves as a memory of motion strategies, providing robust initialization and adaptation across multiple task instances, while MPC ensures safe execution with explicit force tracking, torque feasibility, and collision avoidance. We validate our approach on a torque-controlled manipulator performing industrial deburring tasks. Experiments demonstrate reliable tool insertion, accurate normal force tracking, and circular deburring motions even in hard-to-reach configurations and under obstacle constraints. To our knowledge, this is the first integration of diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial tasks.
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