Reinforcement learning task planner for construction task, assisted by LLM

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Guzmán-Merino, Miguel and Plönnigs, Jörn; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Reinforcement learning task planner for construction task, assisted by LLM. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 344-350. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093238)

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Abstract

Construction sites are non-deterministic environments where traditional tasks planning techniques for multi-robot systems do not work well for long term actions. Constant changes in the environment force a continuous update and evaluation of the state of the system. The paper proposes a reinforcement learning agent assisted by multi-modal foundation model agents to target tasks planning in construction sites. A natural language user request involving tools, consumables, locations and actions is used to command a robot system in the environment. The foundation model agents assist in the identification of relevant information in the user request, the selection of actions, and the object identification. The goal of the system is to execute the desired task at the proper location with the necessary tools and consumables. The proposed system is able to perform in environments with different number of locations and under user requests containing different number of tools, consumables and actions.

  • Item type:Book Section
    ID code:93238
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 09:21
    Last modified:01 Apr 2026 15:11
    URI:https://strathprints.strath.ac.uk/id/eprint/93238
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