Selective exploration and information gathering in search and rescue using hierarchical learning guided by natural language input

Simple item page
dc.contributor.authorPanagopoulos, Dimitrios
dc.contributor.authorPerrusquia, Adolfo
dc.contributor.authorGuo, Weisi
dc.date.accessioned2025-03-06T11:18:38Z
dc.date.available2025-03-06T11:18:38Z
dc.date.freetoread2025-03-06
dc.date.issued2024-10-06
dc.date.pubOnline2025-01-20
dc.description.abstractIn recent years, robots and autonomous systems have become increasingly integral to our daily lives, offering solutions to complex problems across various domains. Their application in search and rescue (SAR) operations, however, presents unique challenges. Comprehensively exploring the disaster-stricken area is often infeasible due to the vastness of the terrain, transformed environment, and the time constraints involved. Traditional robotic systems typically operate on predefined search patterns and lack the ability to incorporate and exploit ground truths provided by human stakeholders, which can be the key to speeding up the learning process and enhancing triage. Addressing this gap, we introduce a system that integrates social interaction via large language models (LLMs) with a hierarchical reinforcement learning (HRL) framework. The proposed system is designed to translate verbal inputs from human stakeholders into actionable RL insights and adjust its search strategy. By leveraging human-provided information through LLMs and structuring task execution through HRL, our approach not only bridges the gap between autonomous capabilities and human intelligence but also significantly improves the agent's learning efficiency and decision-making process in environments characterised by long horizons and sparse rewards.
dc.description.conferencename2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
dc.format.extentpp. 1175-1180
dc.identifier.citationPanagopoulos D, Perrusquia A, Guo W. (2024) Selective exploration and information gathering in search and rescue using hierarchical learning guided by natural language input. In: 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). In: 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 6-10 October 2024, Kuching, Malaysia
dc.identifier.elementsID563505
dc.identifier.urihttps://doi.org/10.1109/smc54092.2024.10831125
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23567
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10831125
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectBehavioral and Social Science
dc.subjectBasic Behavioral and Social Science
dc.titleSelective exploration and information gathering in search and rescue using hierarchical learning guided by natural language input
dc.typeConference paper
dcterms.coverageKuching, Malaysia
dcterms.dateAccepted2024
dcterms.temporal.endDate10 Oct 2024
dcterms.temporal.startDate6 Oct 2024

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
search_and_rescue_using_hierarchical_learning-2025.pdf
Size:
464.88 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)