Actionable Repair is a hands-on collaborative robotics workshop that explores how multimodal AI and human–robot cooperation can make repair more accessible, systematic, and creatively open-ended. The workshop connects high-level repair planning with situated robotic perception and action. Its central contribution is a Multimodal Reasoning-to-Action workflow that translates textual observations, visual documentation, and geometry files into structured Assembly Models describing components, connections, and damage states. These models are linked to generative Action Models, created through structured prompting that defines roles, repair goals, and constraints such as available tools, materials, and robotic capabilities.
The Action Models define step-by-step repair sequences that combine human tasks and robot-executable actions. Robotic perception and control routines enable precise operations such as robotic milling, while complementary repair steps, including filling, surface treatment, or assembly, are performed manually.
Participants will engage with a curated set of damaged architectural artefacts, including chairs, doors, and a small-scale structural element. They prototype and implement collaborative repair interventions through iterative cycles of planning, manual and robotic execution, and evaluation. The workshop concludes with repaired prototypes and documented digital records of the workflows and outcomes.
Workshop Takeaways
- Multimodal Diagnostics: Turn text, images, documents, and scans into graph-based and machine-readable data
- Language to Action Planning: Generate Action-Models that link repair steps to parts and constraints and convert repair intent into robot ready task parameters and candidate toolpaths
- Human-Robot-Collaboration: Define roles, tools, safety, and feedback loops for execution
Participant take aways:
- AI Workflow Use: Run the framework to create and iterate models
- Robotic Workflow Basics: Plan and execute robot enabled actions such as grasping, drilling, or removal while considering kinematics, end effector choice, and workspace setup
- Repair Strategy Skills: Write clear stepwise repair sequences with constraints
- Hands On Prototyping: Test and refine interventions through making and evaluation
Expected Outcomes
- Executable Strategies: Distinct and repeatable repair instructions for human and robot execution
- Robot Toolpaths: Candidate paths generated with LLM support plus simulation validated programs
- Physical Prototypes: Repaired artefacts and one structural element scenario
- Digital Records: Saved Assembly- and Action-Models plus documentation
Tizian Rein, M.A.
Tizian Rein is an architect and doctoral researcher bridging computational design, artificial intelligence and building preservation. During his Master‘s studies, he gained research experience in robotics at TU Munich and ETH Zurich. He is currently a doctoral researcher at the Professorship of Digital Fabrication within the Munich Institute for Robotics and Machine Intelligence (MIRMI) and MIT, USA.
Begüm Saral, M.A.
Begüm Saral is an architect and doctoral researcher specializing in mobile robotics and AI for construction and deconstruction, focused on material reclamation and reuse. She studied at Istanbul Technical University and TU Munich, where she completed her Master’s degree in architecture in 2022. She is currently conducting her research at TUM GNI within the SPAICR project, focusing on implementations of spatial AI for cooperative construction robotics.
Lidia Atansova, Dr.
Lidia Atanasova is an architect and postdoctoral researcher in computational design and digital fabrication. She studied at ITECH University of Stuttgart and TU Wien, earning her Master’s degree in architecture, and worked at TU Wien and in offices in Stuttgart and Vienna. At TUM, she completed her PhD on Human Robot Cooperative Assembly within the CoConstruct innovation network.
Avishek Das, Dr.
Avishek Das is an architect and postdoctoral researcher in robotic and digital fabrication, human–robot collaboration, and XR in design. He studied at Jadavpur University and CEPT University and earned a Fab Academy diploma. He worked as a research Assistant and completed a PhD at Aalborg University, held a postdoc at Aarhus University, and since 2025 is a postdoc at TUM within DFG FOR 5672.
