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.

翻译：模型预测控制（MPC）广泛应用于扭矩控制机器人，但经典公式往往忽略实时力反馈，且在碰撞约束下难以应对高接触性工业任务。去毛刺任务尤其需要精确的刀具插入、稳定的力调节以及在复杂构型下实现无碰撞圆周运动，这超出了标准MPC流水线的能力。我们提出一个框架，将力反馈MPC与基于扩散的运动先验相结合以解决这些挑战。扩散模型作为运动策略的存储器，为多个任务实例提供稳健的初始化和自适应能力，而MPC通过显式力跟踪、扭矩可行性及避障确保安全执行。我们在执行工业去毛刺任务的扭矩控制机械臂上验证了该方法。实验表明，即使在难以触及的构型和障碍约束下，该方法仍能实现可靠的刀具插入、精确的法向力跟踪以及圆周去毛刺运动。据我们所知，这是首次将扩散运动先验与力反馈MPC相结合，用于考虑碰撞的高接触性工业任务。