Robotic architectural assembly with tactile skills: Simulation and optimization: Fid-Bau Portal

Robotic architectural assembly with tactile skills: Simulation and optimization

Belousov, Boris / Wibranek, Bastian / Schneider, Jan / Schneider, Tim / Chalvatzaki, Georgia / Peters, Jan / Tessmann, Oliver

Abstract

Highlights • Reinforcement learning enables robotic assembly of modular architectural structures. • Contact-rich manipulation with tactile feedback facilitates modular assembly. • Grasshopper integration brings reinforcement learning into architectural construction. • Digital design and construction benefit from robot learning and tactile sensing.

Abstract Construction is an industry that could benefit significantly from automation, yet still relies heavily on manual human labor. Thus, we investigate how a robotic arm can be used to assemble a structure from predefined discrete building blocks autonomously. Since assembling structures is a challenging task that involves complex contact dynamics, we propose to use a combination of reinforcement learning and planning for this task. In this work, we take a first step towards autonomous construction by training a controller to place a single building block in simulation. Our evaluations show that trial-and-error algorithms that have minimal prior knowledge about the problem to be solved, so called model-free deep reinforcement learning algorithms, can be successfully employed. We conclude that the achieved results, albeit imperfect, serve as a proof of concept and indicate the directions for further research to enable more complex assemblies involving multiple building elements.