Müge Belek Fialho Teixeira

Human-Robot Collaboration (HRC) in surgery is rarely a straightforward interaction between one human and one robot. In joint replacement surgeries performed with the Mako robot (Makoplasty), the robotic arm enhances surgical precision while also affecting how collaboration unfolds in the operating theater. Utilizing over 80 hours of hospital-based fieldwork, including observations of 15 Makoplasty surgeries and 10 interviews, we examine how the Mako robot shapes surgical HRC. We demonstrate how surgical teams coordinate via tacit cues, negotiated boundaries, and adaptive role shifts across phases of operation. Through vignettes, we highlight the pivotal role of supporting actors, such as nurses and robotic specialists, in maintaining flow and reveal how responsibility is distributed temporally among actors depending on task demands. We argue that surgical HRC is best understood as a multi-actor choreography rather than a surgeon-robot dyad, offering design implications for co-located surgical HRC systems that support distributed collaboration.

The use of recycled materials and locally sourced alternative binders in 3D concrete printing (3DCP) has significant potential to reduce carbon emissions in concrete construction. This study examines the effect of sugarcane bagasse ash (SCBA), a byproducts from the sugarcane industry, as a sustainable binder in 3DCP. SCBA was oven-dried at 105 °C, sieved to 250 µm, and used to replace up to 25% of the total binder by weight in a supplementary cementitious material (SCM) blended system. The impact of polypropylene (PP) and steel (ST) microfibres on SCBA-based mixes was also investigated. The fresh properties of the mortar were evaluated using the flow table, Vicat needle, shape retention, buildability, and rheometer tests. The mortar was 3D printed using a small-scale robotic setup with a RAM extruder. Mechanical properties were then tested, including compressive and flexural strengths, and interlayer bonding, along with microstructure analysis. The results showed that increasing the SCBA content led to greater slump and improved flowability, as well as a slower rate of static yield stress development, with up to a 90 percent reduction compared to the control mix. The addition of PP fibres doubled the static yield stress in the mixes containing 20 percent SCBA. The 10 percent SCBA mix achieved the highest mechanical strength, both in compression and flexure, due to its denser microstructure and enhanced pozzolanic reaction.

Bridging the gap between Artificial Intelligence (AI)-driven generative design and robotic fabrication remains a critical challenge in architectural automation. While Generative Artificial Intelligence (GenAI) tools have advanced conceptual design workflows, their practical deployment in physical construction is hindered by the absence of structured, fabrication-aware datasets to train suitable AI models. This study introduces GDRF (Geometric Data for Robotic Fabrication), an automated pipeline for the generation, evaluation, and encoding of structurally feasible brick wall designs, enabling the creation of machine-learning-compatible data tailored for architectural robotic assembly. We developed a six-stage process that combines parametric modeling, algorithmic design generation, physics-based simulation, data encoding and storage, toolpath generation and assembly simulation, and physical robotic assembly with a robot. Over 33,000 wall configurations were synthetically generated and evaluated for structural stability, of which approximately 52% met the feasibility criteria. Stable and failed designs were identified through displacement-based criteria and encoded using dot-product-based rotational representation, reducing dimensionality while preserving critical geometric features. Comparative analysis revealed that brute-force generation produced more consistent outcomes, while random sampling achieved slightly higher local diversity. This study delivered a data pipeline and the BrickNet dataset, providing a foundation for future research in generative design, structural prediction, and autonomous robotic assembly.

Augmented reality (AR)-enabled human–robot collaboration (HRC) is emerging as a critical paradigm in architectural design and fabrication, particularly for supporting real-time interaction, creative agency, and situated decision-making. As collaborative robots (cobots) become more integrated into exploratory design workflows, AR offers a means to bridge the gap between robotic precision and human intuition. This paper investigates how AR interfaces can facilitate adaptive, embodied collaboration between designers and cobots in spatially unconstrained, exploratory assembly tasks. We developed and evaluated an AR-enabled HRC system across two user studies involving architectural designers. The system allows users to preview, modify, and execute cobotic actions within a shared workspace, incorporating dynamic visual feedback and real-time spatial tracking. Drawing on principles of situated cognition and interactive fabrication, we analyse how AR supports spatial awareness, enhances user agency, and enables intuitive, adaptive interactions. The findings reveal that AR interfaces contribute to HRC through three interconnected themes: (1) improving predictive coordination by externalising cobot intentions and constraints, (2) reinforcing user agency via real-time decision-making tools, and (3) scaffolding situated learning through adaptive visual feedback. We conclude by outlining three key future directions: expanding the spatial and structural complexity of AR-HRC systems, developing more nuanced models of user-cobot interaction in design contexts, and integrating real-time structural feedback to inform user decision-making.

A variety of 3D volumetric scanners and smart-device applications are currently being used in podiatry for recording virtual foot data. The accuracy and reliability of these devices vary, resulting in a large variation in the quality of foot scans used for orthotic design. While it is widely believed that a higher quality scanner yields a better scan and thus is expected to produce a more accurate orthotic design, the direct impact of scanning quality on orthotic design has not yet been tested. Therefore, in this study, three commonly used industrial 3D scanners with varying output qualities were used to obtain foot scans of three participants in two weight-bearing conditions. A total of 54 foot scans were obtained, out of which 18 were used to design orthotic insoles using commercial software (FitFoot360). We found variation in the quality of foot scans produced by the different scanners (61.75 ± 2.23% similarity of the foot scans showing a deviation of less than ±1 mm). However, there were no significant differences in the designed foot orthoses within the same weight-bearing condition (83.59 ± 1.97% similarity of the orthotic designs showing a deviation of less than ±1 mm). The medial arch height and heel width differed significantly only when the weight-bearing condition was changed. The findings from this study suggest that the industrial design and production of an orthotic insole using current methods does not depend on the scanning quality of the scanner used but is dependent on the extent of weight bearing.

The emergence of collaborative robotics presents an opportunity for architectural designers to safely engage in design and fabrication through human-robot collaboration (HRC). By leveraging the adaptability, creativity, and design judgement of designers with the strength, repeatability, and design precision of robotic assistance, HRC has the potential to create a unified design-fabrication workflow. Recent advancements in augmented reality (AR) technology further enhance these prospects by enabling users to superimpose context-sensitive, computer-generated information in the real world. AR technology also provides situational awareness, which proves beneficial in the context of HRC. The maturation of AR technologies offers new possibilities for developing HRC systems tailored to architectural design-fabrication needs. Recognizing the pivotal role of human factors in HRC development process, this paper aims to explore the architectural designers’ needs to develop an AR-enabled HRC system that better supports the fabrication-centric design process, such as exploratory collaborative assembly tasks. Key findings highlight the necessity for a unified design-fabrication workflow, a clearer allocation of tasks between designers and robotic arms, an intuitive user interface, a streamlined interaction process, a better understanding of robot intentions and movements, intuitive procedures for error avoidance and correction, and enhanced user safety in HRC scenarios.