Integrated Approach for Continuous Improvement of Robotic Garments Picking

The recent trend in the textile setting highlights the need for more effective and affordable processes to make the garment market more sustainable. Secondhand garment reuse and fabric recycling are two virtuous pathways toward material saving and sustainable transition. Sorting processes are fundamental enablers of this transition. The main operations involved are identification, classification, and quality assessment. This work proposes a collaborative robotic solution for the first identification and classification of garments to support human experts, along with the following assessment operations for garment and fabric quality and the identification of textile fibers. The proposed approach is based on a collaborative system integrating robots, machine vision models, and dedicated gripper systems to perform the picking actions required to identify and classify garment categories. The first experiments demonstrate the feasibility of the proposed approach as well as the self-learning capability of the system using the detection model continuous training.