Agent-based modeling and simulation (ABMS) has been widely employed to study emergent processes in collective robotic construction (CRC), where global architectural structures arise from local agent interactions. While these approaches reveal how complex assemblies can emerge without centralized control, they remain limited when an architectural goal is known but the behaviors required to achieve it are not. Most CRC workflows still depend on handcrafted heuristics. This paper presents a hybrid CRC workflow that integrates large language models (LLM) into the ABMS behavior design process. The system enables human–AI co-creation of robot behaviors, allowing an LLM agent to generate and negotiate behavioral strategies toward user-defined construction goals under partial observability. The approach is evaluated in simulation, comparing an LLM–heuristic hybrid against a heuristic-only baseline behavior. For well-documented swarm patterns, the LLM matches heuristic performance; for geometrically novel tasks, handcrafted heuristics retain an advantage. By embedding language-based reasoning within ABMS, this work expands participation in CRC behavior design and demonstrates a pathway for translating high-level design intent into adaptive, goal-oriented multiagent construction processes.
Samuel Slezák, Lasath Siriwardena, S. Leder et al.· Construction Robotics· 0 citations
Building disassembly is critical for circular economy material reuse, yet remains rare due to cost and safety constraints, leading to demolition and material downcycling. Automation could improve both efficiency and safety, but currently available technology does not yet enable full automation. We propose a human–robot collaboration system architecture that uses agentic large language models. We test this approach in building disassembly—an unstructured, safety–critical domain where conventional pre-programmed robotics are inadequate. The agentic architecture combines curated domain knowledge, physics simulation for stability validation, and natural language interfaces, enabling the robot to participate through proactive reasoning rather than follow control commands. We evaluated the architecture through three progressively complex scenarios: collaborative spatial adaptation, collaborative decision-making, and learning. The main contribution is a modular, data-grounded HRC methodology in which specialized LLM agents perform agentic reasoning: the robot assesses situations, retrieves relevant procedural knowledge, validates decisions through simulation, and negotiates solutions with human operators. This proof of concept demonstrates that agentic multi-agent LLM systems can enable adaptive human–robot collaboration under uncertainty, beyond natural language interfaces through integrated domain knowledge, physics validation, and agentic reasoning.
Samuel Slezák, Shirin Shevidi, Zahra Shakeri et al.· Construction Robotics· 0 citations
