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.

建筑业可以从自动化中显着受益，但仍然严重依赖手工劳动。因此，我们研究了如何使用机械臂从预定义的离散构建块中自主组装结构。由于组装结构是一项涉及复杂接触动力学的具有挑战性的任务，我们建议将强化学习和规划相结合来完成这项任务。在这项工作中，我们通过训练控制器在模拟中放置单个构建块，迈出了自主构建的第一步。我们的评估表明，可以成功采用对要解决的问题具有最少先验知识的试错算法，即所谓的无模型深度强化学习算法。我们得出的结论是，取得的成果虽然不完美，