Collaborative Robotics with Deformable Materials

TASK AND MOTION PLANNING FOR COLLABORATIVE ROBOTIC CONSTRUCTION WITH DEFORMABLE MATERIALS

Advances in construction automation research are currently validating a shift from industrial machines towards distributed and mobile material-robot construction systems. This means that distributed robotic systems are co-designed directly with the material systems they use. Much like pre-industrial construction, the potential of these systems lies in their adaptability and robustness, allowing them to operate in dynamic environments, to collaborate in large teams, and at potentially unlimited scales. This research synthesises AEC and AI to address the problem of task and motion planning for such multi-machine systems. We will investigate how to combine Reinforcement Learning (RL) with Logic-Geometric Programming (LGP) to form a task and motion planning strategy that can solve collaborative construction problems.

Therefore, the scope of this project includes the co-development of:

• a collaborative material-robot construction system;
• a planning algorithm for sequencing tasks from construction artefacts;
• a system identification methodology for learning material representations;
• the implementation and evaluation of RL and LGP in task and motion planning for the sequenced assembly tasks.

PRINCIPAL INVESTIGATOR

Prof. Achim Menges
Institute for Computational Design and Construction (ICD), University of Stuttgart

RESEARCHER

Nicolas Kubail Kalousdian (ICD)

PARTNER

Prof. Marc Toussaint
Learning and Intelligent Systems Lab (LIS Lab), Technische Universität Berlin

FUNDING

Cyber Valley Research Fund (CyVy-RF)