Enhancing construction robot collaboration via multiagent reinforcement learning

The construction industry necessitates complex interactions among multiple agents (e.g., workers and robots) for efficient task execution. In this paper, we present a framework that aims at achieving multiagent reinforcement learning (RL) for robot control in construction tasks. Our proposed framework leverages the principles of proximal policy optimization (PPO) and develops a multiagent variant to enable robots to acquire sophisticated control policies. We evaluated the effectiveness of our framework through four collaborative construction tasks. The results revealed the efficient collaboration mechanism between agents and demonstrated the ability of our approach to enable multiple robots to learn and adapt their behaviors in complex and dynamic construction tasks while effectively preventing collisions. The results also revealed the advantages of combining RL and inverse kinematics (IK) in enabling precise installation. The findings from this research contribute to the advancement of multiagent RL in the domain of construction robotics. By enabling robots to collaborate effectively, we pave the way for more efficient, flexible, and intelligent construction processes.