The vision of the Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (EXC IntCDC) is to harness the full potential of digital technologies in order to rethink design, fabrication and construction based on integration and interdisciplinarity, with the goal of enabling game-changing innovation in the building sector as it can only occur through highly integrative fundamental research in an interdisciplinary, large-scale research undertaking.

The Cluster aims to lay the methodological foundations for a profound rethinking of the design and building process and related building systems by adopting an integrative computational approach based on interdisciplinary research encompassing architecture, structural engineering, building physics, engineering geodesy, manufacturing and system engineering, computer science and robotics, social sciences and humanities. We aim to bundle the internationally recognised competencies in these fields of the University of Stuttgart and the Max Planck Institute for Intelligent Systems to accomplish our research mission.

The Cluster’s Industry Consortium will ensure direct knowledge exchange, transfer and rapid impact. Taking into account the significant difference between the building industry and other industries, we will tackle the related key challenges of achieving a higher level of integration, performance and adaptability, and we will address the most important building typologies of multi-storey buildings, long-span buildings, and the densification of urban areas.

The Cluster’s broad methodological insights and interdisciplinary findings are expected to result in comprehensive approaches to harnessing digital technologies, which will help to address the ecological, economic and social challenges that current incremental approaches cannot solve.

We envision IntCDC to significantly shape the future of architecture and the building industry through a higher-level integration of computational design and engineering methods, effective cyber-physical (tightly interlinked computational and material) robotic construction processes and new forms of human-machine collaboration, efficient and sustainable next-generation building systems, and socio-cultural and ethical reflection. Thus, the Cluster will have significant impact on creating the conditions required for a liveable and sustainable future built environment, high-quality yet affordable architecture and a novel digital building culture.
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Oct 30, 2026 - EXC IntCDC Research Project 16 'Cyber-Physical On-Site Construction Processes using a Spider Crane Robotic Platform'
Wasserloos, Philipp; Gienger, Andreas; Sawodny, Oliver, 2026, "Replication Data for: Compliance Control of an Electro-Hydraulic Large-Scale Manipulator for Robotic On-Site Construction", https://doi.org/10.18419/DARUS-6488, DaRUS, V1
MATLAB code to replicate the results of the paper titled Compliance Control of an Electro-Hydraulic Large-Scale Manipulator for Robotic On-Site Construction. To evaluate the compliance control scheme, two experiments were carried out: First, a constant force was applied in longitudinal and in lateral direction of the main arm. The data recorded in...
Plain Text - 6.6 KB - MD5: cce9b09fede10057989237576a3a7a16
Code
script to create plots
MATLAB Data - 864.1 KB - MD5: 8a5117434940ccc95f41ed84a02304ca
Data
data recorded from experimental assembly process
MATLAB Data - 2.2 MB - MD5: bb4ac02ff589021b37ad26025212b412
Data
data recorded to validate approach in x-direction of TCP
MATLAB Data - 6.2 MB - MD5: 3e986a0e707cd2f66c22a83f1a8121ae
Data
data recorded to validate approach in y-direction of TCP
Oct 16, 2026 - EXC IntCDC Associated Project 21 'AI-based Timber Construction Process Planing'
Sherkat, Shermin; Wortmann, Thomas; Wortmann, Andreas, 2026, "Software and Scripts for: APTree, Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction", https://doi.org/10.18419/DARUS-6522, DaRUS, V1
This dataset accompanies the paper “APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction” and contains the source code and data needed to reproduce the framework, evaluation, and case study presented in the work. APTree is a model-driven framework for automated robotic task planning in constructi...
Compressed Archive - 271.0 KB - MD5: bfe410b1a8fc25fa3c9ecf492a60d000
Code
Source code of APTree version 1.0 (without replanning): DSL, execution engine, planning service, editor, analysis scripts, Lean proofs. Licensed under MIT. Extract next to the v1.0 data archive and see code/README.md.
Compressed Archive - 127.9 KB - MD5: 9bce667734d9a39450e81a651f8b336e
Data
Data of APTree version 1.0 (without replanning): DSL models, runtime models, PDDL planner inputs, execution logs of the reported run. Licensed under CC BY 4.0. See data/README.md.
Compressed Archive - 272.5 KB - MD5: 52ccea3632338c50f38dd7cfec4ded47
Code
Source code of APTree version 2.0 (with replanning): DSL, execution engine, planning service, editor, analysis scripts, Lean proofs, Docker setup. Licensed under MIT. Extract next to the v2.0 data archive and see code/README.md.
Compressed Archive - 182.8 KB - MD5: bd923c399ff0ee57b6ac623f734c8fcb
Data
Data of APTree version 2.0 (with replanning): DSL models, runtime models, PDDL planner inputs, execution logs of the reported run. Licensed under CC BY 4.0. See data/README.md.
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