The Master in Robotics and Advanced Construction (MRAC) seeks to train a new generation of interdisciplinary professionals who are capable of facing our growing need for a more sustainable and optimised construction ecosystem. The Master is focused on the emerging design and market opportunities arising from novel robotic and advanced manufacturing systems.
Through a mixture of seminars, workshops, and studio projects, the master programme challenges the traditional processes in the Construction Sector. It investigates how advances in robotics and digital fabrication tools change the way we build and develop processes and design tools for such new production methods.
Rethinking Robotics Through Experience Reflections on a lecture by Madeline Gannon Introduction “Somehow, someway, I have built a career out of misusing technology.” Madeline Gannon has spent over a decade working at the intersection of industrial robotics, art, and human interaction. This text presents a thematic synthesis of her lecture for the MRAC, followed by … Read more
This article presents my Workshop 1.2 individual assignment, which focuses on analyzing data and translating it into robotic movements to control an ABB IRB 6700-150/3.20 industrial robot equipped with a plastic pellet extruder for additive manufacturing. The core concept of the project involved studying the drying and shrinkage of the Aral Sea over time, extracting … Read more
IAAC Workshop led by the Professorship of Digital Fabrication | Technical University of Munich Faculty: Begüm Saral, Avishek Das and Pit Siebenaler Design Goals and Process When designing with the bricks, we began looking at their imperfections. We thought about using and exposing these defects as part of the material’s architectural expression. The idea focused … Read more
The global construction industry currently faces a significant sustainability crisis, characterized by the massive generation of material waste that often bypasses recycling streams. This challenge stems primarily from a technological gap: the lack of real-time identification, sorting, and decision-making systems on active demolition sites. Without an efficient way to distinguish between various debris types, valuable … Read more
This article presents a project exploring how collected data can be processed and translated into automated classification and robotic decision-making logic. The core concept revolves around studying the physical condition and spatial arrangement of reclaimed construction material, extracting geometry-based and surface-level information to generate a data-driven sorting and retrieval workflow. By combining artificial intelligence with … Read more
RE:PAIR –Perception-Driven Closed-Loop Robotic Repair Faculty: Hamid Peiro and Aleksandra Kraeva (Sasha) Mission Statement: RE:PAIR is a perception-driven, decision-visible closed-loop robotic system that detects cracks, repairs them using robotic 3D printing, and autonomously verifies the result. 1) Crack detection 2) Segmentation and Masking 3) Centreline and points First we outlayed the workflow of the project. … Read more
Solutions for Rapid Data Acquisition in Disaster Scenarios This project explores a robotic perception pipeline designed to support post-disaster assessment and early response scenarios. The core objective is to detect and map cracks in damaged buildings using vision-based data, assisting rescue teams by providing spatial information about potentially unsafe areas. Rather than aiming for full … Read more
In the following blog post, we will analyse the development of the concept of our project carried out in Workshop 2.1. Team Collaborative Workflow and Modular System Development Guide, in which a Rotatory Gantry structure is proposed for the flip-type movement of a robotic arm. AIRFLIP – Rotary Gantry System for Small-Scale Cobots Overview The … Read more
“Spatial Cognition in Robotics: Optimizing Construction Workflow with AI”Shu Xiao – MRAC02 Thesis, IAAC 2024–2025 Despite advances in manufacturing automation, the construction industry continues to rely heavily on manual labor due to its unpredictable environment, safety concerns, and fragmented workflows. This project explores how vision-guided robotics and real-time AI agents can close that gap by … Read more
“Precision becomes poetry through reflection and sound.” Reflection is an immersive installation that explores the potential of precision and reflection between two robotic arms through synchronized movements. Equipped with mirrors, the robots become both instruments and performers. Concept: The law of reflection The law of reflection states that the angle of incidence is equal to … Read more
INTRODUCTION In today’s construction industry, a large portion of timber is discarded due to its non-standard shape, curvature, or internal defects. Un_Log Factory challenges this paradigm by proposing a digitally augmented fabrication system that embraces the natural irregularity of timber. Instead of seeing bent or cracked logs as waste, our process redefines them as raw … Read more
A Real-Time Audio-Visual Stone Symphony Rock the Rock is an interactive audio-visual installation that identifies and tracks rocks in real time, generating dynamic sound and projection overlays. By leveraging computer vision and finite state machines, it transforms geological forms into a sensory experience. Concept and Context This project served as our introduction to Finite State … Read more
In the ongoing pursuit of efficient toolpath generation, we set out to build a modular, logic-driven system capable of planning subtractive manufacturing strategies. The framework integrated zonal segmentation, directional movement, and dynamic state awareness; its goal was adaptability and long-term scalability. While we achieved full system functionality, this initial iteration exposed the distance between a … Read more
| INTRODUCTION | Learning Structures: From Parametric Rules to Machine-Made Forms. In this project, we explored the interplay between parametric design, machine learning, and robotic fabrication. Starting from Kapla block assemblies controlled by simple deformation rules, we trained a GAN to reinterpret and generate new structural variations. The process concluded with robotic pick-and-place construction, closing … Read more
Github : https://github.com/Clarrainl/UN_LOG-Factory | INTRODUCTION | Detecting wood defects in 3D-scanned logs using Machine Learning In the timber industry, a significant portion of wood gets discarded due to irregularities or defects that make it unusable under standard practices. However, many of these logs can still be used creatively or structurally if properly understood and classified. This project … Read more
