MARC: A multi-agent robots control framework for enhancing reinforcement learning in construction tasks

TL;DR

This paper introduces MARC, a multi-agent reinforcement learning framework for robot control in construction tasks, enabling effective collaboration, complex interaction handling, and collision prevention among multiple robots.

Contribution

It develops a multi-agent reinforcement learning framework based on proximal policy optimization tailored for construction robotics, demonstrating improved coordination and adaptability.

Findings

Robots learned to collaborate effectively in construction tasks.

The framework prevented collisions among multiple robots.

Reinforcement learning combined with inverse kinematics enhanced control policies.

Abstract

Letting robots emulate human behavior has always posed a challenge, particularly in scenarios involving multiple robots. In this paper, we presented a framework aimed at achieving multi-agent reinforcement learning for robot control in construction tasks. The construction industry often necessitates complex interactions and coordination among multiple robots, demanding a solution that enables effective collaboration and efficient task execution. Our proposed framework leverages the principles of proximal policy optimization and developed a multi-agent version to enable the robots to acquire sophisticated control policies. We evaluated the effectiveness of our framework by learning four different collaborative tasks in the construction environments. The results demonstrated the capability of our approach in enabling multiple robots to learn and adapt their behaviors in complex construction tasks while effectively preventing collisions. Results also revealed the potential of combining and exploring the advantages of reinforcement learning algorithms and inverse kinematics. The findings from this research contributed to the advancement of multi-agent reinforcement learning in the domain of construction robotics. By enabling robots to behave like human counterparts and collaborate effectively, we pave the way for more efficient, flexible, and intelligent construction processes.