Robotic architectural assembly with tactile skills: Simulation and optimization

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.

Automated summary

Research shows that a combination of reinforcement learning and planning can be used to autonomously assemble structures with a robotic arm, and trial-and-error algorithms have proven effective in this field. Although the achieved results are imperfect, they serve as a proof of concept and indicate directions for further research.