Integrating LLMs into Collaborative Robotics for Automated Zipped Apparel Disassembly

In order to successfully integrate circular economy principles into current value chains, it is crucial to ensure the economic sustainability of disassembly processes. Recent scientific and technological innovations, including Large Language Models (LLMs) in artificial intelligence (AI), are greatly accelerating progress in enhancing robotic capabilities. Dismantling is the first step in the re-manufacturing, repair, and recycling processes of end-of-life (EoL) products. Traditionally, this operation is performed manually by operators or by expensive dedicated robotic cells. Although manual disassembly offers flexibility in handling complex situations, it is a time-consuming and labour-intensive process that can negatively affect the health of operators and the cost-effectiveness of the disassembly process. However, robot disassembly presents difficulties in handling complex parts in a flexible manner. Robot application emerges as an automated versatile solution, capable of handling uncertainties in the frequency, quantity, and quality of end-of-life products. A fully automated approach for the removal of clothing zips from garments is proposed. The approach detects the pixels corresponding to the zip using LLM-Based AI techniques to plan the path of the robotic arm. The solution reduces the effort of AI training in industrial applications. Simulations validate the approach and its performance.