{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T03:55:08Z","timestamp":1781582108074,"version":"3.54.5"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009473","name":"Unversity of M\u00e1laga","doi-asserted-by":"publisher","award":["JA.B1-09"],"award-info":[{"award-number":["JA.B1-09"]}],"id":[{"id":"10.13039\/100009473","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009473","name":"Unversity of M\u00e1laga","doi-asserted-by":"publisher","award":["PID2020-117057GB-I00"],"award-info":[{"award-number":["PID2020-117057GB-I00"]}],"id":[{"id":"10.13039\/100009473","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009473","name":"Unversity of M\u00e1laga","doi-asserted-by":"publisher","award":["PID2023-148191NB-I00"],"award-info":[{"award-number":["PID2023-148191NB-I00"]}],"id":[{"id":"10.13039\/100009473","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministry of Science, Innovation and Universities of Spain","doi-asserted-by":"publisher","award":["JA.B1-09"],"award-info":[{"award-number":["JA.B1-09"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministry of Science, Innovation and Universities of Spain","doi-asserted-by":"publisher","award":["PID2020-117057GB-I00"],"award-info":[{"award-number":["PID2020-117057GB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministry of Science, Innovation and Universities of Spain","doi-asserted-by":"publisher","award":["PID2023-148191NB-I00"],"award-info":[{"award-number":["PID2023-148191NB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Large Language Models (LLMs) provide cognitive capabilities that enable robots to interpret and reason about their workspace, especially when paired with semantically rich representations like semantic maps. However, these models are prone to generating inaccurate or invented responses, known as hallucinations, that can produce an erratic robotic operation. This can be addressed by employing agentic workflows, structured processes that guide and refine the model\u2019s output to improve response quality. This work formally defines and qualitatively analyzes the impact of three agentic workflows (LLM Ensemble, Self-Reflection, and Multi-Agent Reflection) on enhancing the reasoning capabilities of an LLM guiding a robotic system to perform object-centered planning. In this context, the LLM is provided with a pre-built semantic map of the environment and a query, to which it must respond by determining the most relevant objects for the query. This response can be used in a multitude of downstream tasks. Extensive experiments were carried out employing state-of-the-art LLMs and semantic maps generated from the widely-used datasets ScanNet and SceneNN. The results show that agentic workflows significantly enhance object retrieval performance, especially in scenarios requiring complex reasoning, with improvements averaging up to 10% over the baseline.<\/jats:p>","DOI":"10.3390\/robotics14030024","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T07:46:57Z","timestamp":1740383217000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Agentic Workflows for Improving Large Language Model Reasoning in Robotic Object-Centered Planning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8677-5126","authenticated-orcid":false,"given":"Jesus","family":"Moncada-Ramirez","sequence":"first","affiliation":[{"name":"Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4123-7330","authenticated-orcid":false,"given":"Jose-Luis","family":"Matez-Bandera","sequence":"additional","affiliation":[{"name":"Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3845-3497","authenticated-orcid":false,"given":"Javier","family":"Gonzalez-Jimenez","sequence":"additional","affiliation":[{"name":"Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9929-5309","authenticated-orcid":false,"given":"Jose-Raul","family":"Ruiz-Sarmiento","sequence":"additional","affiliation":[{"name":"Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1729881419839596","DOI":"10.1177\/1729881419839596","article-title":"A review of mobile robots: Concepts, methods, theoretical framework, and applications","volume":"16","author":"Rubio","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Galindo, C., Saffiotti, A., Coradeschi, S., Buschka, P., Fernandez-Madrigal, J.A., and Gonzalez, J. (2005, January 2\u20136). Multi-Hierarchical Semantic Maps for Mobile Robotics. Proceedings of the 2005 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada.","DOI":"10.1109\/IROS.2005.1545511"},{"key":"ref_3","first-page":"335","article-title":"3D Mapping with Semantic Knowledge","volume":"Volume 4020","author":"Wulf","year":"2006","journal-title":"RoboCup 2005: Robot Soccer World Cup IX"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ranganathan, A., and Dellaert, F. (2007, January 27\u201330). Semantic Modeling of Places using Objects. Proceedings of the Robotics: Science and Systems III, Atlanta, GA, USA.","DOI":"10.15607\/RSS.2007.III.001"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.robot.2008.03.008","article-title":"Curious George: An attentive semantic robot","volume":"56","author":"Meger","year":"2008","journal-title":"Robot. Auton. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1017\/S0269888900007797","article-title":"Ontologies: Principles, methods and applications","volume":"11","author":"Uschold","year":"1996","journal-title":"Knowl. Eng. Rev."},{"key":"ref_7","unstructured":"Lassila, O., and Swick, R.R. (2024, December 23). Resource Description Framework (RDF) Model and Syntax Specification. World Wide Web Consortium (W3C) Recommendation. Available online: https:\/\/www.w3.org\/TR\/1999\/REC-rdf-syntax-19990222\/."},{"key":"ref_8","unstructured":"McGuinness, D.L., and van Harmelen, F. (2024, December 23). OWL Web Ontology Language Overview. W3C Recommendations. Available online: https:\/\/www.w3.org\/TR\/owl-features\/."},{"key":"ref_9","unstructured":"Prud\u2019hommeaux, E., and Seaborne, A. (2024, December 23). SPARQL Query Language for RDF. W3C Recommendations 2008. Available online: https:\/\/www.w3.org\/TR\/rdf-sparql-query\/."},{"key":"ref_10","unstructured":"Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., and Dean, M. (2024, December 23). SWRL: A Semantic Web Rule Language Combining OWL and RuleML. W3C Member Submission. Available online: https:\/\/www.w3.org\/submissions\/SWRL\/."},{"key":"ref_11","unstructured":"Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., and Wilkins, D. (2024, December 23). PDDL: The Planning Domain Definition Language. Yale Center for Computational Vision and Control, Tech Report CVC TR-98-003\/DCS TR-1165. Available online: https:\/\/www.cs.cmu.edu\/~mmv\/planning\/readings\/98aips-PDDL.pdf."},{"key":"ref_12","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_13","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2024, December 23). Improving Language Understanding by Generative Pre-Training. OpenAI Tech. Rep. Available online: https:\/\/www.mikecaptain.com\/resources\/pdf\/GPT-1.pdf."},{"key":"ref_14","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA."},{"key":"ref_15","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2024, December 23). Language Models are Unsupervised Multitask Learners. OpenAI Tech. Rep. Available online: https:\/\/cdn.openai.com\/better-language-models\/language_models_are_unsupervised_multitask_learners.pdf."},{"key":"ref_16","unstructured":"OpenAI (2023). GPT-4 Technical Report. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gu, Q., Kuwajerwala, A., Morin, S., Jatavallabhula, K.M., Sen, B., Agarwal, A., Rivera, C., Paul, W., Ellis, K., and Chellappa, R. (2024, January 13\u201317). ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610243"},{"key":"ref_18","unstructured":"Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., and Suenderhauf, N. (2023, January 6\u20139). SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning. Proceedings of the Conference on Robot Learning (CoRL), Atlanta, GA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gao, T., Fisch, A., and Chen, D. (2021, January 1\u20136). Making Pre-trained Language Models Better Few-shot Learners. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Virtual Event.","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3571730","article-title":"Survey of Hallucination in Natural Language Generation","volume":"55","author":"Ji","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_21","unstructured":"Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., and Chen, W. (2023). Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback. arXiv."},{"key":"ref_22","unstructured":"Patil, S.G., Zhang, T., Wang, X., and Gonzalez, J.E. (2023). Gorilla: Large Language Model Connected with Massive APIs. arXiv."},{"key":"ref_23","unstructured":"Schick, T., Dwivedi-Yu, J., Dess\u00ec, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. arXiv."},{"key":"ref_24","unstructured":"Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., and Yang, Y. (2023, January 10\u201316). Self-Refine: Iterative Refinement with Self-Feedback. Proceedings of the 37th International Conference on Neural Information Processing Systems, Orleans, LA, USA."},{"key":"ref_25","unstructured":"Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., and Yao, S. (2023, January 10\u201316). Reflexion: Language Agents with Verbal Reinforcement Learning. Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hua, B.S., Pham, Q.H., Nguyen, D.T., Tran, M.K., Yu, L.F., and Yeung, S.K. (2016, January 25\u201328). SceneNN: A Scene Meshes Dataset with aNNotations. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.18"},{"key":"ref_28","unstructured":"Matez-Bandera, J.L., Ojeda, P., Monroy, J., Gonzalez-Jimenez, J., and Ruiz-Sarmiento, J.R. (2024). Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/TSMCB.2008.920227","article-title":"Multihierarchical Interactive Task Planning: Application to Mobile Robotics","volume":"38","author":"Galindo","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.knosys.2016.12.016","article-title":"Building Multiversal Semantic Maps for Mobile Robot Operation","volume":"119","author":"Galindo","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., Pham, T.T., Latif, Y., Milford, M., and Reid, I. (2016, January 9\u201314). Meaningful Maps with Object-Oriented Semantic Mapping. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2017.8206392"},{"key":"ref_32","unstructured":"McCormac, J., Handa, A., Davison, A., and Leutenegger, S. (June, January 29). SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"R\u00fcnz, M., Buffier, M., and Agapito, L. (2018, January 16\u201320). MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects. Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Munich, Germany.","DOI":"10.1109\/ISMAR.2018.00024"},{"key":"ref_34","unstructured":"Qian, J., Chatrath, V., Yang, J., Servos, J., Schoellig, A.P., and Waslander, S.L. (July, January 27). POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes. Proceedings of the Robotics: Science and Systems XVIII, New York, NY, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qian, J., Chatrath, V., Servos, J., Mavrinac, A., Burgard, W., Waslander, S.L., and Schoellig, A.P. (2023, January 10\u201314). POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments. Proceedings of the Robotics: Science and Systems XIX, Daegu, Republic of Korea.","DOI":"10.15607\/RSS.2023.XIX.069"},{"key":"ref_36","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_38","unstructured":"Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., and Hausman, K. (2022, January 14\u201318). Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. Proceedings of the 6th Conference on Robot Learning (CoRL), Auckland, New Zealand."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Silver, T., Dan, S., Srinivas, K., Tenenbaum, J.B., Kaelbling, L.P., and Katz, M. (2024, January 20\u201327). Generalized Planning in PDDL Domains with Pretrained Large Language Models. Proceedings of the 38th AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i18.30006"},{"key":"ref_40","unstructured":"Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., and Stone, P. (2023). LLM+P: Empowering Large Language Models with Optimal Planning Proficiency. arXiv."},{"key":"ref_41","unstructured":"Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., and Chebotar, Y. (2022, January 14). Inner Monologue: Embodied Reasoning through Planning with Language Models. Proceedings of the 6th Conference on Robot Learning (CoRL), Auckland, New Zealand."},{"key":"ref_42","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., and Ray, A. (December, January 28). Training Language Models to Follow Instructions with Human Feedback. Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Madaan, A., Tandon, N., Rajagopal, D., Clark, P., Yang, Y., and Hovy, E. (2021, January 7\u201311). Think About It! Improving Defeasible Reasoning by First Modeling the Question Scenario. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Virtual Event.","DOI":"10.18653\/v1\/2021.emnlp-main.508"},{"key":"ref_44","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., and Zhou, D. (December, January 28). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA."},{"key":"ref_45","unstructured":"Chang, K., Xu, S., Wang, C., Luo, Y., Xiao, T., and Zhu, J. (2024). Efficient Prompting Methods for Large Language Models: A Survey. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, K., Tian, Y., Peng, N., and Klein, D. (2022, January 7\u201311). Re3: Generating Longer Stories with Recursive Reprompting and Revision. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, United Arab Emirates.","DOI":"10.18653\/v1\/2022.emnlp-main.296"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., and Cong, X. (2024, January 11\u201316). ChatDev: Communicative Agents for Software Development. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Bangkok, Thailand.","DOI":"10.18653\/v1\/2024.acl-long.810"},{"key":"ref_48","unstructured":"Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S.K.S., and Lin, Z. (2024, January 7\u201311). MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_49","unstructured":"Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., and Liu, J. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv."},{"key":"ref_50","unstructured":"Zhang, J., Xiang, J., Yu, Z., Teng, F., Chen, X., Chen, J., Zhuge, M., Cheng, X., Hong, S., and Wang, J. (2024, January 7\u201311). AFlow: Automating Agentic Workflow Generation. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_51","unstructured":"Fan, S., Cong, X., Fu, Y., Zhang, Z., Zhang, S., Liu, Y., Wu, Y., Lin, Y., Liu, Z., and Sun, M. (2024). WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models. arXiv."},{"key":"ref_52","unstructured":"Qiao, S., Fang, R., Qiu, Z., Wang, X., Zhang, N., Jiang, Y., Xie, P., Huang, F., and Chen, H. (2024, January 7\u201311). Benchmarking Agentic Workflow Generation. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_53","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020, January 6\u201312). Language Models are Few-Shot Learners. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Online."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bender, E.M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021, January 3\u201310). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), Virtual Event.","DOI":"10.1145\/3442188.3445922"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"12539","DOI":"10.1109\/LRA.2022.3220156","article-title":"Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera Under Uncertainty","volume":"7","author":"Monroy","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Morilla-Cabello, D., Mur-Labadia, L., Martinez-Cantin, R., and Montijano, E. (2023, January 1\u20135). Robust Fusion for Bayesian Semantic Mapping. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10342253"},{"key":"ref_57","unstructured":"Jang, J., Ye, S., and Seo, M. (2022, January 3). Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts. Proceedings of the NeurIPS 2022 Workshop on Transfer Learning for Natural Language Processing, New Orleans, LA, USA."},{"key":"ref_58","unstructured":"Anil, R., Borgeaud, S., Alayrac, J.B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., Millican, K., and Silver, D. (2023). Gemini: A Family of Highly Capable Multimodal Models. arXiv."},{"key":"ref_59","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., and Azhar, F. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1162\/tacl_a_00638","article-title":"Lost in the Middle: How Language Models Use Long Contexts","volume":"12","author":"Liu","year":"2024","journal-title":"Trans. Assoc. Comput. Linguist."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/3\/24\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:41:21Z","timestamp":1760028081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/3\/24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["robotics14030024"],"URL":"https:\/\/doi.org\/10.3390\/robotics14030024","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]}}}