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Autonomous Collaborative: Robotic Reconfiguration with Deep Multi-Agent Reinforcement Learning [ACRR+DMARL]

Hosmer, Tyson; Mutis, Sergio; Hughes, Eric; He, Ziming; Siedler, Philipp; Gheorghiu, Octavian; Erdinçer, Barış; (2023) Autonomous Collaborative: Robotic Reconfiguration with Deep Multi-Agent Reinforcement Learning [ACRR+DMARL]. In: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy - Proceedings of the 43rd Annual Conference of the Association for Computer Aided Design in Architecture, ACADIA 2023. (pp. pp. 72-90). ACADIA: Denver, Colorado, USA. Green open access

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Abstract

To address the unprecedented challenges of the global climate and housing crises, requires a radical change in the way we conceive, plan, and construct buildings, from static continuous objects to adaptive eco-systems of reconfigurable parts. Living systems in nature demonstrate extraordinary scalable efficiencies in adaptive construction with simple flexible parts made from sustainable materials. The interdisciplinary field of collective robotic construction (CRC) inspired by natural builders has begun to demonstrate potential for scalable, adaptive, resilient, and low-cost solutions for building construction with simple robots. Yet, to explore the opportunities inspired by natural systems, CRC systems must be developed utilizing artificial intelligence for collaborative and adaptive construction, which has yet to be explored. Autonomous Collaborative Robotic Reconfiguration (ACRR) is a robotic material system with an adaptive lifecycle trained with deep, multi-agent reinforcement learning (DMARL) for collaborative reconfiguration. Autonomous Collaborative Robotic Reconfiguration is implemented through three interrelated components codesigned in relation to each other: 1) a reconfigurable robotic material system; 2) a cyber-physical simulation, sensing, and control system; and 3) a framework for collaborative robotic intelligence with DMARL. The integration of the CRC system with bidirectional cyber-physical control and collaborative intelligence enables ACRR to operate as a scalable and adaptive architectural eco-system. It has the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment moving from automated toward autonomous construction.

Type: Proceedings paper
Title: Autonomous Collaborative: Robotic Reconfiguration with Deep Multi-Agent Reinforcement Learning [ACRR+DMARL]
Event: ACADIA 2023: Habits of the Anthropocene
Open access status: An open access version is available from UCL Discovery
Publisher version: https://2023.acadia.org/
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture
URI: https://discovery.ucl.ac.uk/id/eprint/10200300
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