From Junk to Genius: Robotic Arms and AI Crafting Creative Designs from Scraps

As sustainable architecture is increasingly emphasizing material reuse, this study proposes a novel, interactive workflow that integrates robotic arms and artificial intelligence to transform waste materials from architectural models into creative design components. Unlike existing recycling efforts, which focus on the construction phase, this research uniquely targeted discarded architectural model materials, particularly polystyrene foam, that are often overlooked, despite their environmental impact. The workflow combined computer vision and machine learning, utilizing the YOLOv5 model, which achieved a classification accuracy exceeding 83% for the polygon, rectangle, and circle categories, demonstrating a superior recognition performance. Robotic sorting demonstrated the ability to process up to six foam blocks per minute under controlled conditions. By integrating Stable Diffusion, we further generated speculative architectural renderings, enhancing creativity and design exploration. Participant testing revealed that human interaction reduced stacking errors by 57% and significantly improved user satisfaction. Moreover, human–robot collaboration not only corrected robotic errors, but also fostered innovative and collaborative solutions, demonstrating the system’s potential as a versatile tool for education and industry while promoting sustainability in design. Thus, this workflow offers a scalable approach to creative material reuse, promoting sustainable practices from the model-making stage of architectural design. While these initial results are promising, further research is needed to adapt this technique for larger-scale construction materials, addressing real-world constraints and broadening its applicability.

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