DecompGrind: A Decomposition Framework for Robotic Grinding via Cutting-Surface Planning and Contact-Force Adaptation

2026-03-24

AI Summary

Existing robotic automated polishing methods struggle to efficiently adapt to variations in workpiece geometry and material properties and often require extensive training data to model material removal dynamics and deformation. This work proposes a decomposed polishing framework that decouples the task into Global Cutting Surface Planning (GCSP) and Local Contact Force Adaptation (LCFA). GCSP leverages geometric analysis to generate high-precision polishing trajectories, while LCFA employs imitation learning with bilateral control to achieve safe and compliant force regulation. By significantly reducing reliance on large training datasets, the proposed approach demonstrates high accuracy and safety in automatic polishing across a variety of 3D-printed workpieces with diverse shapes and hardness levels, thereby validating its effectiveness and generalization capability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Robotic grinding is widely used for shaping workpieces in manufacturing, but it remains difficult to automate this process efficiently. In particular, efficiently grinding workpieces of different shapes and material hardness is challenging because removal resistance varies with local contact conditions. Moreover, it is difficult to achieve accurate estimation of removal resistance and analytical modeling of shape transition, and learning-based approaches often require large amounts of training data to cover diverse processing conditions. To address these challenges, we decompose robotic grinding into two components: removal-shape planning and contact-force adaptation. Based on this formulation, we propose DecompGrind, a framework that combines Global Cutting-Surface Planning (GCSP) and Local Contact-Force Adaptation (LCFA). GCSP determines removal shapes through geometric analysis of the current and target shapes without learning, while LCFA learns a contact-force adaptation policy using bilateral control-based imitation learning during the grinding of each removal shape. This decomposition restricts learning to local contact-force adaptation, allowing the policy to be learned from a small number of demonstrations, while handling global shape transition geometrically. Experiments using a robotic grinding system and 3D-printed workpieces demonstrate efficient robotic grinding of workpieces having different shapes and material hardness while maintaining safe levels of contact force.

Problem

• robotic grinding
• removal resistance
• shape transition
• contact-force adaptation
• automation

Innovation

• Decomposition Framework
• Cutting-Surface Planning
• Contact-Force Adaptation
• Imitation Learning
• Robotic Grinding