Design processes, in many disciplines like architecture, civil engineering or mechanical engineering, involve navigating large, high-dimensional and heterogeneous data. While AI-driven approaches like inverse design and surrogate modeling can enhance design exploration, their adoption is hindered by complex workflows and the need for coding and machine learning expertise. To address this, we introduce AI-eXtended Design (AIXD): a low-code, open-source toolbox that integrates AI into computational design. AIXD simplifies handling of mixed data types, as well as the analysis, training, and deployment of machine learning models for inverse design, surrogate modeling, and sensitivity analysis, enabling domain experts to rapidly explore diverse solutions with minimal coding.In this paper, we show the functionalities of the toolbox, and we demonstrate AIXD’s capabilities in architectural and engineering design applications, showing how it accelerates performance evaluation, generates high-performing alternatives, and improves design understanding by delivering new insights. By bridging AI and design practice, AIXD lowers the entry barrier to data-driven methods, making AI-extended design more accessible and efficient.

This paper presents Interactive Robotic Plastering (IRoP), a system enabling designers and skilled workers to engage intuitively with an in-situ robotic plastering process. The research combines three elements: interactive design tools, an augmented reality interface, and a robotic spraying system. Plastering is a complex process relying on tacit knowledge and craftsmanship, making it difficult to simulate and automate. However, our system utilizes a controller-based interaction system to enable diverse users to interactively create articulated plasterwork in-situ. A customizable computational toolset converts human intentions into robotic motions while respecting robotic and material constraints. To accomplish this, we developed both an interactive computational model to translate the data from a motion-tracking system into robotic trajectories using design and editing tools as well as an audio-visual guidance system for in-situ projection. We then conducted two user-studies of designers and skilled workers who used IRoP to design and fabricate a full-scale demonstrator.

This paper presents an interactive design system that allows the user to create and fabricate stylized sculptures in water-based clay, using a standard 6-axis robot arm. This system facilitates the materialization of abstract design intentions into clay, through the algorithmic formulation of sculpting styles, the optimal path planning of the sculpting toolpaths, and a subtractive robotic fabrication process using customized tools. Unlike other precision-driven fabrication technologies, the authors embrace artistic uncertainty by conducting manual and robotic sculpting experiments and incorporating prominent parameters that affect the fabrication quality. The versatility of the described approach is demonstrated by designing a series of sculpting styles over a wide range of 3D models and robotically fabricating them in clay. Additionally, the paper explores various strategies for designing stylized robotic sculpting patterns by generating toolpaths informed by different techniques.

Abstract We present a computational technique that aids with the design of structurally‐sound metal frames, tailored for robotic fabrication using an existing process that integrate automated bar bending, welding, and cutting. Aligning frames with structurally‐favorable orientations, and decomposing models into fabricable units, we make the fabrication process scale‐invariant, and frames globally align in an aesthetically‐pleasing and structurally‐informed manner. Relying on standard analysis of frames, we then co‐optimize the shape and topology of bars at the local unit level. At this level, we minimize combinations of functional and aesthetic objectives under strict fabrication constraints that model the assembly of discrete sets of bent bars. We demonstrate the capabilities of our global‐to‐local approach on four robotically‐constructed examples.

In this paper we present a system for the in situ33In the context of building construction, “in situ” means that fabrication takes place at the structure's final location directly on the building site. fabrication of a full-scale, load-bearing, and doubly-curved steel reinforced concrete wall. Two complementary vision-based sensing systems provide the feedback necessary to build a 12 meter long steel wire mesh as part of a novel digital building process. The sensing systems provide estimates of the robot pose, referenced to the CAD model of the building site, as well as feedback on the accuracy of the built structure over the course of construction. This second piece of information is used to adapt the building plan to compensate for system inaccuracies and material deformations which occur during buildup. In this way, the structure was successfully built with 98% of the total geometry within 2 centimeters of the designed position. To the best of our knowledge, this is the largest structure which has been built by a mobile robot using solely vision-based sensing.

Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline to detect arbitrarily placed objects in a scene and form a structure out of the detected objects. The next best stacking pose is selected using a searching method employing gradient descent with random initial orientations, exploiting a physics engine. This approach is validated in an experimental setup using a robotic manipulator by constructing balancing vertical stacks without mortars and adhesives. We show the results of eleven consecutive trials to form such towers autonomously using four arbitrarily in front of the robot placed rocks.

In the reordering buffer management problem a sequence of requests arrive online in a finite metric space, and have to be processed by a single server. This server is equipped with a request buffer of size k and can decide at each point in time, which request from its buffer to serve next. Servicing of a request is simply done by moving the server to the location of the request. The goal is to process all requests while minimizing the total distance that the server is traveling inside the metric space. In this paper we present a deterministic algorithm for the reordering buffer management problem that achieves a competitive ratio of O(log Delta + min {log n,log k}) in a finite metric space of n points and aspect ratio Delta. This is the first algorithm that works for general metric spaces and has just a logarithmic dependency on the relevant parameters. The guarantee is memory-robust, i.e., the competitive ratio decreases only slightly when the buffer-size of the optimum is increased to h=(1+\epsilon)k. For memory robust guarantees our bounds are close to optimal.

Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline that detects randomly placed objects in a scene that are used by our next best stacking pose searching method employing gradient descent with a random initial orientation, exploiting a physics engine. This approach is validated in an experimental setup using a robotic manipulator by constructing balancing vertical stacks without mortars and adhesives. We show the results of eleven consecutive trials to form such towers autonomously using four arbitrarily in front of the robot placed rocks.

Despite the recent advances in, and the adoption of robotic technologies in the construction industry, the architectural processes which demand a high degree of geometric freedom still remain largely labour intensive and manual. This is due to the inherent difficulties in robotizing the current implementation of such processes coupled with the lack of alternate robotic technologies. A specific example, which is also the focus of this paper, is that of building a steel reinforced concrete structure, with varying curvature or cross-section. This process still remains rather manual and requires extensive support of customized form-work. In this paper, first we describe an alternate novel robotic fabrication process for building steel wire meshes which act as both reinforcement and formwork. The robotization of such a process is discussed with the use of a previously developed mobile robotic system. Based on the specifications derived from the process, design of a novel custom designed robotic end-effector, enabling this process, is detailed. Automation of the full robotic system comprising the mobile robotic system and the robotic end-effector is discussed from simulation to control. Through experimental evaluation of the robotic system, we demonstrate the ability to fully automate the construction of non-standard steel reinforced steel meshes of varying curvature and cell sizes.

Despite the prevalent use of robotic technologies in industrial manufacturing, their use on building construction sites is still very limited. This is mainly due to the unstructured nature of construction sites and the fact that the structures built must be larger than the machines which build them. This paper addresses these difficulties by presenting a repositioning and localization system which, using purely on-board sensing, allows a mobile robot to maintain a high degree of end-effector positioning accuracy while moving among numerous building positions during a building task. We introduce a newly developed machine, called the In situ Fabricator (IF), whose goal is to bring digital fabrication to the construction site. The capabilities of the repositioning and localization system is demonstrated through the construction of a vertical stack of bricks, in which the IF repositions itself after placing each brick. With this experiment, we demonstrate that the IF can build with sub-centimeter accuracy over long building sequences.

This paper presents the building of lightweight tensile structures with quadrocopters. The construction elements (such as ropes, cables, and wires) in this kind of structure are subject to tension forces. This paper identifies the basic building elements (nodes, links) required for the construction of tensile structures, and translates them into meaningful trajectories for quadrocopters. The use of a library of building elements is suggested. Hybrid force-position control strategies based on admittance control are exploited. Prototypical tensile structures are built by quadrocopters to validate the proposed approach. An accompanying video shows the building process.

In this paper, viable applications for mobile robotic units on construction sites are explored. While identifying potential areas for in-situ fabrication in the construction sector, the intention is also to build upon innovative man-machine interaction paradigms to deal with the imprecision and tolerances often faced on construction sites. By combining the precision of the machine with the innate cognitive human skills, a simple but effective mobile fabrication system is tested for the building of algorithmically designed structures that would not be possible through conventional manual means. It is believed that this new approach to man-machine collaboration, aimed at a deeper integration of human ability with the strengths of digitally controlled machines, will result in advances in the construction sector, thus opening up new design and application fields for architects and planners.