Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring

TL;DR

This paper introduces a novel framework combining force-feedback Model Predictive Control with diffusion-based motion priors to enhance robotic deburring tasks, ensuring precise force regulation and obstacle avoidance.

Contribution

It is the first to integrate diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial robot tasks.

Findings

Reliable tool insertion achieved in complex configurations.

Accurate normal force tracking demonstrated.

Circular deburring motions performed successfully with obstacle constraints.

Abstract

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.