{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T12:10:39Z","timestamp":1760443839338,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,25]]},"DOI":"10.1145\/3748777.3748807","type":"proceedings-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T11:53:38Z","timestamp":1760442818000},"page":"145-148","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LLM-Powered Embodied Intelligence for Socially-Aware Robot Navigation in Human-Robot Interaction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5183-8266","authenticated-orcid":false,"given":"Xuqing","family":"Liu","sequence":"first","affiliation":[{"name":"The University of Osaka, Suita, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5140-0121","authenticated-orcid":false,"given":"Ahmed","family":"Farid","sequence":"additional","affiliation":[{"name":"Waseda University, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8011-247X","authenticated-orcid":false,"given":"Tatsuya","family":"Amano","sequence":"additional","affiliation":[{"name":"The University of Osaka, Suita, Japan and RIKEN Center for ComputationalScience, Kobe, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8278-8801","authenticated-orcid":false,"given":"Hamada","family":"Rizk","sequence":"additional","affiliation":[{"name":"The University of Osaka, Suita, Japan and RIKEN Center for ComputationalScience, Kobe, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2273-4876","authenticated-orcid":false,"given":"Hirozumi","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"The University of Osaka, Suita, Japan and RIKEN Center for ComputationalScience, Kobe, Japan"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"Michael Ahn Anthony Brohan and Others. 2022. Do As I Can Not As I Say: Grounding Language in Robotic Affordances. arxiv:https:\/\/arXiv.org\/abs\/2204.01691\u00a0[cs.RO] https:\/\/arxiv.org\/abs\/2204.01691"},{"key":"e_1_3_3_1_3_2","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et\u00a0al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877\u20131901."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10611018"},{"key":"e_1_3_3_1_5_2","unstructured":"Gheorghe Comanici Eric Bieber and Others. 2025. Gemini 2.5: Pushing the Frontier with Advanced Reasoning Multimodality Long Context and Next Generation Agentic Capabilities. arxiv:https:\/\/arXiv.org\/abs\/2507.06261\u00a0[cs.CL]"},{"key":"e_1_3_3_1_6_2","unstructured":"DeepSeek-AI Daya Guo Dejian Yang Haowei Zhang et\u00a0al. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arxiv:https:\/\/arXiv.org\/abs\/2501.12948\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2501.12948"},{"key":"e_1_3_3_1_7_2","series-title":"Proceedings of Machine Learning Research","first-page":"1","volume-title":"Proceedings of the 1st Annual Conference on Robot Learning","volume":"78","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy, German Ros, Felipe Codevilla, and Others. 2017. CARLA: An Open Urban Driving Simulator. In Proceedings of the 1st Annual Conference on Robot Learning(Proceedings of Machine Learning Research, Vol.\u00a078), Sergey Levine, Vincent Vanhoucke, and Ken Goldberg (Eds.). PMLR, 1\u201316."},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Henrik Kretzschmar Markus Spies Christoph Sprunk and Wolfram Burgard. 2016. Socially compliant mobile robot navigation via inverse reinforcement learning. International Journal of Robotics Research 35 11 (Sept. 2016) 1289\u20131307. doi:10.1177\/0278364915619772","DOI":"10.1177\/0278364915619772"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Henrik Kretzschmar Markus Spies Christoph Sprunk and Wolfram Burgard. 2016. Socially compliant mobile robot navigation via inverse reinforcement learning. Int. J. Rob. Res. 35 11 (Sept. 2016) 1289\u20131307. doi:10.1177\/0278364915619772","DOI":"10.1177\/0278364915619772"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160660"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561595"},{"key":"e_1_3_3_1_12_2","unstructured":"Yizhou Liu Pengfei Gao Xinchen Wang and Others. 2024. MarsCode Agent: AI-native Automated Bug Fixing. arxiv:https:\/\/arXiv.org\/abs\/2409.00899\u00a0[cs.SE]"},{"key":"e_1_3_3_1_13_2","unstructured":"Zhao Mandi Shreeya Jain and Shuran Song. 2023. RoCo: Dialectic Multi-Robot Collaboration with Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2307.04738\u00a0[cs.RO] https:\/\/arxiv.org\/abs\/2307.04738"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Christoforos Mavrogiannis Francesca Baldini Allan Wang Dapeng Zhao Pete Trautman Aaron Steinfeld and Jean Oh. 2023. Core Challenges of Social Robot Navigation: A Survey. ACM Transactions on Human-Robot Interaction 12 3 Article 36 (April 2023) 39\u00a0pages. doi:10.1145\/3583741","DOI":"10.1145\/3583741"},{"key":"e_1_3_3_1_15_2","volume-title":"7th Annual Conference on Robot Learning","author":"Shah Dhruv","year":"2023","unstructured":"Dhruv Shah, Michael Equi, Blazej Osinski, Fei Xia, Brian Ichter, and Sergey Levine. 2023. Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning. In 7th Annual Conference on Robot Learning. https:\/\/openreview.net\/forum?id=PsV65r0itpo"},{"key":"e_1_3_3_1_16_2","unstructured":"Chien-Yao Wang I-Hau Yeh and Hong-Yuan\u00a0Mark Liao. 2024. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. doi:10.48550\/ARXIV.2402.13616"}],"event":{"name":"SSTD '25: 19th International Symposium on Spatial and Temporal Data","location":"Osaka Japan","acronym":"SSTD '25"},"container-title":["Proceedings of the 19th International Symposium on Spatial and Temporal Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3748777.3748807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T11:55:35Z","timestamp":1760442935000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3748777.3748807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,25]]},"references-count":15,"alternative-id":["10.1145\/3748777.3748807","10.1145\/3748777"],"URL":"https:\/\/doi.org\/10.1145\/3748777.3748807","relation":{},"subject":[],"published":{"date-parts":[[2025,8,25]]},"assertion":[{"value":"2025-10-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}