DeepCloud: the application of a data-driven generative model in design

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design

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Bidgoli, Ardavan, and Pedro Veloso. “DeepCloud: The Application of a Data-Driven Generative Model in Design.” Recalibration: On Imprecision and Infidelity Paper Proceedings Book for the 2018 Association of Computer Aided Design in Architecture Conference, IngramSpark, 2018, pp. 176–85.

Image Classification for Robotic Plastering with Convolutional Neural Network

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Inspecting robotically fabricated objects to detect and classify dis- crepancies between virtual target models and as-built realities is one of the challenges that faces robotic fabrication. Industrial-grade computer vision methods have been widely used to detect manufacturing flaws in mass pro- duction lines. However, in mass-customization, a versatile and robust method should be flexible enough to ignore construction tolerances while detecting specified flaws in varied parts. This study aims to leverage recent developments in machine learning and convolutional neural networks to improve the resiliency and accuracy of surface inspections in architectural robotics. Under a supervised learning scenario, the authors compared two approaches: (1) transfer learning on a general purpose Convolutional Neural Network (CNN) image classifier, and (2) design and train a CNN from scratch to detect and categorize flaws in a robotic plastering workflow. Both CNNs were combined with conventional search methods to improve the accuracy and efficiency of the system. A web- based graphical user interface and a real-time video projection method were also developed to facilitate user interactions and control over the workflow.

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Joshua, et al. “Image Classification for Robotic Plastering with Deep Neural Networks.” Robotic Fabrication in Architecture, Art and Design 2018, Springer, Cham, 2018, pp. 3–15,

Assisted Automation: Three Learning Experiences in Architectural Robotics

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Fueled by long-standing dreams of both material efficiency and aesthetic liberation, robots have become part of mainstream architectural discourses, raising the question: How may we nurture an ethos of visual, tactile, and spatial exploration in technologies that epitomize the legacies of industrial automation—for example, the pursuit of managerial efficiency, control, and an ever-finer subdivision of labor? Reviewing and extending a growing body of research on architectural robotics pedagogy, and bridging a constructionist tradition of design education with recent studies of science and technology, this article offers both a conceptual framework and concrete strategies to incorporate robots into architectural design education in ways that foster a spirit of exploration and discovery, which is key to learning creative design. Through reflective accounts of three learning experiences, we introduce the notions “assisted automation” and “robotic embodiment” as devices to enrich current approaches to robot–human design, highlighting situated and embodied aspects of designing with robotic machines.

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Cardoso Llach, Daniel, et al. “Assisted Automation: Three Learning Experiences in Architectural Robotics.” International Journal of Architectural Computing, vol. 15, no. 1, SAGE Publications Sage UK: London, England, 2017, pp. 87–102.

Of Hands and Robots: ‘Assisted Automation’ and ‘Robotic Enactments’ in Creative Robotics Pedagogy

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This paper explores ways to incorporate robots into design education, especially in architecture, in ways that privilege students' visual, tactile and spatial engagement with design problems. The paper is informed by constructionist theories of learning and studies of science and technology (STS) re-thinking agency as relational and distributed. We use these lenses to document two introductory learning experiences in architectural robotics, conducted as part of an architecture graduate course. The exercises combine robotics, scripting, model-making and sketching in ways that emphasize, and take advantage of, designers' visual, spatial, and material sensibilities, as well as the contingent nature of human-robot encounters.

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Cardoso Llach, Daniel, et al. “Assisted Automation: Three Learning Experiences in Architectural Robotics.” International Journal of Architectural Computing, vol. 15, no. 1, SAGE Publications Sage UK: London, England, 2017, pp. 87–102.

Towards an Integrated Design-Making Approach in Architectural Robotics

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Using industrial robots in creative design has generated a wide interest among designers, artists, and architects. While generic, combined with custom or task-specific mounted tools and digital descriptions, these machines have recently become the vehicle of creative explorations in design, architecture, and the arts. Even though numerous researchers and practitioners have proposed applications of robotics in architectural practice, this field is still in its infancy and thus needs more exploration by design and architectural researchers. In this thesis, I have investigated the architectural robotics opportunities by reviewing its design space and characteristics in academia and practice. It resulted in a hypothesis stating that currently available software toolboxes are not sufficient mediums between architects and robots. Accordingly, we need a medium to embed all the constraints that affect a specific robotic system, its mounted tool, and related material system, from early stages of design to materialization. To test this hypothesis, I proposed an analytical grammar to codify spatial design, form finding process, and robotic fabrication behavior through visual computation and algorithmic approaches. The system affordances were later studied through physical prototypes.

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Bidgoli, Ardavan. Towards an Integrated Design-Making Approach in Architectural Robotics. Pennsylvania State University, 2015.

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Towards a Motion Grammar for Robotic Stereotomy

This paper presents progress towards the definition of a mo- tion grammar for robotic stereotomy. It describes a vocabulary of mo- tions able to generate complex forms by cutting, slicing, and/or carving 3-D blocks of material using a robotic arm and a custom made cutting tool. While shape grammars usually deal with graphical descriptions of designs, a motion grammar seeks to address the 3-D harmonic move- ments of machine, tool, and material substrate choreographically, sug- gesting motion as a generative vehicle of exploration in both designing and making. Several models and prototypes are presented and dis- cussed.

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Bidgoli, Ardavan, and Daniel Cardoso Llach. “Towards A Motion Grammar for Robotic Stereotomy.” Emerging Experience in Past, Present, and Future of Digital Architecture, Proceedings of the 20th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2015, pp. 723–32.