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

This research is rooted in the transdisciplinary field of cybernetics, significantly advanced in the 1960s and 1970s by figures like Gordon Pask, Cedric Price, Nicholas Negroponte, Christopher Alexander, John Frazer, and the Archigram group. Gordon Pask argued that the architect’s role is less about designing static structures and more about catalyzing environments that evolve over time. He challenged the traditional view of architecture as a fixed material artifact, instead proposing it as a dynamic composition of interrelated systems regulated by feedback in a constantly changing environment. Projects such as Cedric Price’s Fun Palace (1961) and Generator (1976) were designed as intelligent, adaptive architectural environments that could reconfigure themselves using mobile cranes controlled by cybernetic systems. Both Pask and Price focused on managing “indeterminacy,” exploring architecture’s capacity to adapt to and influence its inhabitants.

This approach to architecture is especially relevant in today’s rapidly transforming world, with the construction industry increasingly pressurized by the global climate crisis, global housing shortage, increasing nomadic and aging populations, and a growing shortage of skilled labor. Our human population is expected to grow to 8.6 billion in 2030, 9.8 billion in 2050, and 11.2 billion in 2100, requiring us to build over two billion homes in the next 80 years.6 Meanwhile, an estimated 33% of the world’s waste, and at least 40% of the world’s carbon dioxide emissions is produced by the construction industry. While technological advances in computation, robotics, and AI have rapidly transformed other industries such as the automotive industry, the construction industry is notoriously slow in its uptake of disruptive technologies, while being one of the least digitized sectors. Most buildings are organized and constructed as a layered system called sheering layers and designed with linear building life cycles often resulting in buildings which are over specified and inflexible to future changes in demand and eventually ending in demolition. Industrial research in computation, AI, and robotics for construction tends to be segmented within isolated processes at different stages of building design, fabrication, and construction. To address the unprecedented challenges we are facing requires radically changing the way we conceive, plan, and construct buildings, from linear processes used to produce static continuous objects to interrelational processes of developing reconfigurable buildings with adaptive life cycles.

Living systems in nature exhibit highly adaptive and efficient construction through the flexible assembly of simple parts made of sustainable materials. The extraordinary collective intelligence and robust and adaptive building techniques found in nature have inspired new multi-disciplinary approaches to construction with multi-robot systems categorized as collective robotic construction (CRC). CRC specifically concerns embodied, autonomous, multirobot systems that modify a shared environment according to high-level, user-specified goals integrating architectural design, the construction process, mechanisms, and control. By codesigning robots together with building systems, we can develop more adaptive, scalable, and reusable architecture.

The research presented in this paper builds on the notable advantages seen in collective construction of natural builders and growing research in CRC. The scope of this research is to establish and test a design framework for autonomous collaborative robotic construction (ACRC) with modular robotic material eco-systems (MRMES) trained with deep multi-agent reinforcement learning (DMARL). This design framework 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 DMARL / world models. In this paper, we establish the framework and then implement it through three case studies for collaborative modular robotic assembly of reconfigurable building parts.