Reinforcement learning for scaffold-free construction of spanning structures

Abstract

In construction robotics, a conventional design-to-fabrication workflow starts with designing a structure, followed by task and robotic motion planning, and ultimately, fabrication. However, this approach can prove unsuccessful, as we may only discover the infeasibility of a design at the final stages of the process. This can result in rework and a considerable waste of time and resources. To overcome this challenge, we propose a design method based on reinforcement learning (RL) where the agent makes decisions at every step of the sequential assembly of the structure while considering assembly’s stability. In this way, we take the construction constraints into consideration at the design stage. The research particularly focuses on the design of spanning structures that multiple robot arms can construct without the need for scaffolding.