Benchmarking Large Language Model Reasoning in Indoor Robot Navigation

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dc.authoridSarıgül, Mehmet/0000-0001-7323-6864
dc.contributor.authorBalci, Emirhan
dc.contributor.authorSarigul, Mehmet
dc.contributor.authorAta, Baris
dc.date.accessioned2026-02-27T07:33:30Z
dc.date.available2026-02-27T07:33:30Z
dc.date.issued2025
dc.description33rd Conference on Signal Processing and Communications Applications-SIU-Annual
dc.description.abstractThis study evaluates the performance of state-of-the-art text-based generative large language models in indoor robot navigation planning, focusing on object, spatial, and common-sense reasoning-centric instructions. Three scenes from the Matterport3D dataset were selected, along with corresponding instruction sequences and routes. Object-labeled semantic maps were generated using the RGB-D images and camera poses of the scenes. The instructions were provided to the models, and the generated robot codes were executed on a mobile robot within the selected scenes. The routes followed by the robot, which detected objects through the semantic map, were recorded. The findings indicate that while the models successfully executed object and spatial-based instructions, some models struggled with those requiring common-sense reasoning. This study aims to contribute to robotics research by providing insights into the navigation planning capabilities of language models.
dc.identifier.doi10.1109/SIU66497.2025.11111749
dc.identifier.isbn979-8-3315-6656-2; 979-8-3315-6655-5
dc.identifier.issn2165-0608
dc.identifier.urihttp://dx.doi.org/10.1109/SIU66497.2025.11111749
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4614
dc.identifier.wosWOS:001575462500002
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2025 33rd Signal Processing and Communications Applications Conference, Siu
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectLarge Language Models
dc.subjectRobotics
dc.subjectNavigation
dc.subjectPrompt Engineering
dc.titleBenchmarking Large Language Model Reasoning in Indoor Robot Navigation
dc.typeProceedings Paper

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