Autonomous ecologies of construction: Collaborative modular robotic material eco-systems with deep multi-agent reinforcement learning

Abstract

To address the unprecedented challenges of construction pressurized by the global climate crisis, housing shortage, and growing shortage of skilled labor, this research presents a radical shift in the construction lifecycle of buildings, from linear processes that produce static continuous buildings to interrelational processes linking adaptative eco-systems of collaborative robots and reconfigurable building parts. Inspired by natural builders, the interdisciplinary field of collective robotic construction (CRC) offers the potential for scalable, adaptive, and resilient construction with simple robots. We establish a design framework for autonomous collaborative robotic construction (ACRC) through modular robotic material eco-systems (MRMES) trained with deep multi-agent reinforcement learning (DMARL). This involves the integration of three core aspects: (1) modular robotic material eco-systems (2) cyber-physical simulation and control with bidirectional feedback (3) adaptive intelligence through deep multi-agent reinforcement learning. The framework is implemented through three comparable case studies for collaborative modular robotic assembly of reconfigurable building parts.