期刊
AUTOMATION IN CONSTRUCTION
卷 133, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2021.104006
关键词
Discrete design; Robots in architecture; Reinforcement learning
资金
- European Union [640554]
- Forum for Interdisciplinary Research at TU Darmstadt
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.
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.
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