@misc{14042,
  abstract     = {{This paper investigates co-creativity in architectural robotics through
a design-research experiment that repositions the robot from a fabrication tool
to an active collaborator in spatial exploration. A turn-based assembly process
enables a human designer and a robotic arm to alternately place modular bricks
within a shared three-dimensional environment. Each move is evaluated through
computational rules—support, stability, and collision—and interpreted in real
time via an AI image-to-image diffusion model. This sequential exchange
establishes a reciprocal design dialogue: the human reads the robot’s action as
a spatial proposition, while the robot responds to the evolving structure.
Creative agency becomes distributed across human and machine, encouraging
reflection on intent and positioning robotic intelligence as a source of suggestion
rather than execution. By prioritizing responsiveness and interpretation over
efficiency, the research challenges conventional file-to-factory paradigms and
frames robotics as an active participant in design thinking. It contributes to
discussions on human–machine collaboration by proposing co-creative robotics
as a framework for rethinking authorship in architecture.
Keywords. Human–robot Collaboration, Co-Creation, Computational
Creativity, Robotic Fabrication, Artificial Intelligence, Digital Fabrication,
Generative Models, Discrete Assembly, Human–Machine Interaction.}},
  author       = {{Sachs, Hans and Aykin, Yusuf and Sardenberg, Victor and Schneider, Bo}},
  booktitle    = {{eCAADe 2026 - Informed creativity in architecture and engineering}},
  editor       = {{eCAADe, eCaade}},
  keywords     = {{Co Creative Robots, Collaborative Robots, Design, Architecture}},
  location     = {{Lübeck}},
  pages        = {{357--366}},
  publisher    = {{CumInCAD}},
  title        = {{{Co-Creative Robotics Turn-Based Human–Robot Assembly for Architectural Design Exploration}}},
  volume       = {{44}},
  year         = {{2026}},
}

@misc{14036,
  abstract     = {{This paper presents a robotic system developed to enhance
automation in construction workflows through advanced AI
and computer vision technologies. The system integrates
a robotic arm with a 3D point cloud camera and state-
of-the-art 2D pre-trained Deep Learning models, such as
GroundingDINO and SegmentAnything, to detect and seg-
ment construction elements in 3D dynamic, unstructured
environments. By processing point cloud data from the cam-
era and aligning it with real-world coordinates, the system
achieves precise object localization, enabling tasks such as
element handling and assembly. Designed to address chal-
lenges like clutter, occlusion, and variability in construction
sites, this system bridges the gap between controlled labora-
tory conditions and real-world applications. Experimental
evaluations highlight its potential to improve efficiency and
adaptability in construction tasks.}},
  author       = {{Aykin, Yusuf and Sachs, Hans and Gerzen, Nikolai}},
  booktitle    = {{Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)}},
  editor       = {{Sachs, Hans}},
  issn         = {{2413-5844}},
  keywords     = {{Computer Vision, Architecture, Building Construction, Robotics in Construction}},
  publisher    = {{International Association for Automation and Robotics in Construction (IAARC)}},
  title        = {{{Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction}}},
  doi          = {{10.22260/isarc2025/0026}},
  year         = {{2025}},
}