Geometric Point-Cloud Perception and Defect-Aware Grasping Framework for Industrial Workpieces

Industrial robots in small-batch manufacturing and human–robot collaborative workstations are increasingly required to perform grasping and sorting tasks on workpieces with natural language instructions. Unlike generic object grasping, industrial workpieces may contain local surface defects such as dents, scratches, and geometric irregularities, which affect both target selection and grasp stability. This paper presents a geometric point-cloud perception and defect-aware grasping framework that leverages 3D geometric cues measured from point clouds to detect surface defects and guide robotic manipulation. Rather than learning an end-to-end visuomotor policy, the proposed framework introduces an explicit geometric reasoning layer between language-level task specification and robotic grasp execution. The language model is used only to convert instructions into structured task attributes, whereas defect perception, target ranking, and grasp-contact evaluation are performed using measurable 3D geometric evidence. These components require no task-specific training on annotated industrial defect or grasping datasets, making the formulation potentially useful for small-batch manufacturing scenarios in which large annotated defect datasets are unavailable. A shared defect-response representation supports both instance-level target selection and contact-region screening. We evaluate the proposed method on a controlled prototype tabletop setup, achieving an average defect instance-level F1-score of 0.89, a target grounding accuracy of 0.89, and a grasp success rate of 84.0%.