{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:51:15Z","timestamp":1781189475445,"version":"3.54.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006116","name":"Advanced Manufacturing Office","doi-asserted-by":"publisher","award":["DE-EE0009726"],"award-info":[{"award-number":["DE-EE0009726"]}],"id":[{"id":"10.13039\/100006116","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10845-023-02294-y","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T11:02:23Z","timestamp":1704798143000},"page":"1141-1157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1808-2660","authenticated-orcid":false,"given":"Haolin","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7922-9451","authenticated-orcid":false,"given":"Xuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-7460","authenticated-orcid":false,"given":"Jerry Ying Hsi","family":"Fuh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4022-6912","authenticated-orcid":false,"given":"Wen Feng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6140-4189","authenticated-orcid":false,"given":"Bingbing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"2294_CR1","unstructured":"Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Ho, D., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jang, E., Ruano, R. J., Jeffrey, K., ... Zeng, A. (2022). Do as I can, not as I say: Grounding language in robotic affordances. arXiv:2204.01691"},{"key":"2294_CR2","unstructured":"Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., Chu, E., Clark, J. H., El Shafey, L., Huang, Y., Meier-Hellstern, K., Mishra, G., Moreira, E., Omernick, M., Robinson, K., ..., Wu, Y. (2023). Palm 2 technical report. arXiv:2305.10403"},{"key":"2294_CR3","unstructured":"Anthropic. (2023). Model card and evaluations for Claude models. https:\/\/www-files.anthropic.com\/production\/images\/Model-Card-Claude-2.pdf"},{"key":"2294_CR4","unstructured":"Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., & Sutton, C. (2021). Program synthesis with large language models. arXiv:2108.07732"},{"key":"2294_CR5","doi-asserted-by":"crossref","unstructured":"Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., Do, Q. V., Xu, Y., & Fung, P. (2023). A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. arXiv:2302.04023","DOI":"10.18653\/v1\/2023.ijcnlp-main.45"},{"issue":"5","key":"2294_CR6","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.3390\/app12052319","volume":"12","author":"S-O Bezrucav","year":"2022","unstructured":"Bezrucav, S.-O., & Corves, B. (2022). Modelling automated planning problems for teams of mobile manipulators in a generic industrial scenario. Applied Sciences, 12(5), 2319. https:\/\/doi.org\/10.3390\/app12052319","journal-title":"Applied Sciences"},{"key":"2294_CR7","doi-asserted-by":"crossref","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jackson, T., Jesmonth, S., Joshi, N. J., Julian, R., Kalashnikov, D., Kuang, Y., ..., Zitkovich, B. (2023). RT-1: Robotics transformer for real-world control at scale. arXiv:2212.06817","DOI":"10.15607\/RSS.2023.XIX.025"},{"key":"2294_CR8","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Arvind, N., Pranav, S., Girish, S., Amanda, A., Sandhini, A., Ariel, H.-V., Gretchen, K., Tom, H., Rewon, C., Aditya, R., Daniel, Z., Jeffrey, W., Clemens, W., ..., Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in neural information processing systems (Vol. 33, pp. 1877\u20131901). Curran Associates, Inc."},{"key":"2294_CR9","doi-asserted-by":"publisher","first-page":"102484","DOI":"10.1016\/j.rcim.2022.102484","volume":"81","author":"A Buerkle","year":"2023","unstructured":"Buerkle, A., Eaton, W., Al-Yacoub, A., Zimmer, M., Kinnell, P., Henshaw, M., Coombes, M., Chen, W.-H., & Lohse, N. (2023). Towards industrial robots as a service (IRAAS): Flexibility, usability, safety and business models. Robotics and Computer-Integrated Manufacturing, 81, 102484. https:\/\/doi.org\/10.1016\/j.rcim.2022.102484","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"2294_CR10","unstructured":"Capitanelli, A., & Mastrogiovanni, F. (2023). A framework to generate neurosymbolic PDDL-compliant planners. arXiv:2303.00438"},{"key":"2294_CR11","doi-asserted-by":"crossref","unstructured":"Chen, J.-T., & Huang, C.-M. (2023). Forgetful large language models: Lessons learned from using LLMS in robot programming. arXiv:2310.06646","DOI":"10.1609\/aaaiss.v2i1.27721"},{"key":"2294_CR12","unstructured":"Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., ..., Zaremba, W. (2021). Evaluating large language models trained on code. arXiv:2107.03374"},{"key":"2294_CR13","unstructured":"Chen, P.-L., & Chang, C.-S. (2023). Interact: Exploring the potentials of ChatGPT as a cooperative agent. arXiv:2308.01552"},{"key":"2294_CR14","doi-asserted-by":"crossref","unstructured":"Choi, D., Shi, W., Liang, Y. S., Yeo, K. H., & Kim, J. -J. (2021). Controlling industrial robots with high-level verbal commands. In Social robotics (pp. 216\u2013226). Springer International Publishing.","DOI":"10.1007\/978-3-030-90525-5_19"},{"key":"2294_CR15","first-page":"3761","volume":"33","author":"C Colas","year":"2020","unstructured":"Colas, C., Karch, T., Lair, N., Dussoux, J.-M., Moulin-Frier, C., Dominey, P., & Oudeyer, P.-Y. (2020). Language as a cognitive tool to imagine goals in curiosity driven exploration. Advances in Neural Information Processing Systems, 33, 3761\u20133774.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2294_CR16","unstructured":"Coumans, E., & Bai, Y. (2016\u20132021). PyBullet, a python module for physics simulation for games, robotics and machine learning. http:\/\/pybullet.org"},{"key":"2294_CR17","unstructured":"Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMS. arXiv:2305.14314"},{"key":"2294_CR18","unstructured":"Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., ..., Florence, P. (2023). PaLM-E: An embodied multimodal language model. arXiv:2303.03378"},{"issue":"32","key":"2294_CR19","doi-asserted-by":"publisher","first-page":"e2123433119","DOI":"10.1073\/pnas.2123433119","volume":"119","author":"I Drori","year":"2022","unstructured":"Drori, I., Zhang, S., Shuttleworth, R., Tang, L., Lu, A., Ke, E., Liu, K., Chen, L., Tran, S., Cheng, N., Wang, R., Singh, N., Patti, T. L., Lynch, J., Shporer, A., Verma, N., Wu, E., & Strang, G. (2022). A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proceedings of the National Academy of Sciences, 119(32), e2123433119. https:\/\/doi.org\/10.1073\/pnas.2123433119","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2294_CR20","doi-asserted-by":"publisher","unstructured":"Goel, R., & Gupta, P. (2020). Robotics and industry 4.0. A roadmap to industry 4.0: Smart production, sharp business and sustainable development (pp. 157\u2013169). https:\/\/doi.org\/10.1007\/978-3-030-14544-6_9","DOI":"10.1007\/978-3-030-14544-6_9"},{"key":"2294_CR21","doi-asserted-by":"publisher","unstructured":"H\u00e4gele, M., Nilsson, K., Pires, J. N., & Bischoff, R. (2016). Industrial robotics. In Springer handbook of robotics (pp. 1385\u20131422). https:\/\/doi.org\/10.1007\/978-3-319-32552-1_54","DOI":"10.1007\/978-3-319-32552-1_54"},{"key":"2294_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02211-3","author":"L Heuss","year":"2023","unstructured":"Heuss, L., Gebauer, D., & Reinhart, G. (2023). Concept for the automated adaption of abstract planning domains for specific application cases in skillsbased industrial robotics. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-023-02211-3","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"2294_CR23","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s10845-021-01826-8","volume":"34","author":"T Hoebert","year":"2021","unstructured":"Hoebert, T., Lepuschitz, W., Vincze, M., & Merdan, M. (2021). Knowledge-driven framework for industrial robotic systems. Journal of Intelligent Manufacturing, 34(2), 771\u2013788. https:\/\/doi.org\/10.1007\/s10845-021-01826-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2294_CR24","unstructured":"Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Ka, S., Yau, S., Lin, Z., Zhou, L., Ran, C., Xiao, L., & Wu, C. (2023). MetaGPT: Meta programming for multi-agent collaborative framework. arXiv:2308.00352"},{"issue":"3","key":"2294_CR25","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s10723-022-09618-x","volume":"20","author":"H Hu","year":"2022","unstructured":"Hu, H., Chen, J., Liu, H., Li, Z., & Huang, L. (2022). Natural language-based automatic programming for industrial robots. Journal of Grid Computing, 20(3), 26\u201344. https:\/\/doi.org\/10.1007\/s10723-022-09618-x","journal-title":"Journal of Grid Computing"},{"key":"2294_CR26","doi-asserted-by":"publisher","first-page":"10608","DOI":"10.1109\/ICRA48891.2023.10160969","volume":"2023","author":"C Huang","year":"2023","unstructured":"Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023a). Visual language maps for robot navigation. IEEE International Conference on Robotics and Automation (ICRA), 2023, 10608\u201310615. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10160969","journal-title":"IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"2294_CR27","unstructured":"Huang, S., Jiang, Z., Dong, H., Qiao, Y., Gao, P., & Li, H. (2023b). Instruct2Act: Mapping multimodality instructions to robotic actions with large language model. arXiv:2305.11176"},{"key":"2294_CR28","unstructured":"Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022). Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International conference on machine learning (pp. 9118\u20139147)."},{"key":"2294_CR29","unstructured":"Huang, W., Wang, C., Zhang, R., Li, Y., Wu, J., & Fei-Fei, L. (2023c). VoxPoser: Composable 3D value maps for robotic manipulation with language models. arXiv:2307.05973"},{"key":"2294_CR30","unstructured":"Huang, W., Xia, F., Shah, D., Driess, D., Zeng, A., Lu, Y., Florence, P., Mordatch, I., Levine, S., Hausman, K., & Ichter, B. (2023d). Grounded decoding: Guiding text generation with grounded models for robot control. arXiv:2303.00855"},{"key":"2294_CR31","unstructured":"Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., Sermanet, P., Jackson, T., Brown, N., Luu, L., Levine, S., Hausman, K., & Ichter, B. (2023e). Inner monologue: Embodied reasoning through planning with language models. In K. Liu, D. Kulic, & J. Ichnowski (Eds.), Proceedings of the 6th conference on robot learning (Vol. 205, pp. 1769\u20131782). PMLR."},{"key":"2294_CR32","unstructured":"Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., Finn, C., & Finn, C. (2022). BC-Z: Zeroshot task generalization with robotic imitation learning. In Proceedings of the 5th conference on robot learning (pp. 991\u20131002)."},{"key":"2294_CR33","unstructured":"Jiang, Y., Gu, S., Murphy, K., & Finn, C. (2019). Language as an abstraction for hierarchical deep reinforcement learning. arXiv:1906.07343"},{"key":"2294_CR34","unstructured":"Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2023). Large language models are zero-shot reasoners. arXiv:2205.11916"},{"key":"2294_CR35","doi-asserted-by":"publisher","unstructured":"Kollar, T., Tellex, S., Roy, D., & Roy, N. (2010). Toward understanding natural language directions. In 2010 5th ACM\/IEEE international conference on human\u2013robot interaction (HRI) (pp. 259\u2013266). https:\/\/doi.org\/10.1109\/HRI.2010.5453186","DOI":"10.1109\/HRI.2010.5453186"},{"key":"2294_CR36","doi-asserted-by":"publisher","unstructured":"Kollar, T., Tellex, S., Roy, D., & Roy, N. (2014). Grounding verbs of motion in natural language commands to robots. In Experimental robotics: The 12th international symposium on experimental robotics (pp. 31\u201347). https:\/\/doi.org\/10.1007\/978-3-642-28572-1_3","DOI":"10.1007\/978-3-642-28572-1_3"},{"key":"2294_CR37","unstructured":"Kwon, M., Xie, S. M., Bullard, K., & Sadigh, D. (2023). Reward design with language models. arXiv:2303.00001"},{"key":"2294_CR38","doi-asserted-by":"publisher","first-page":"9493","DOI":"10.1109\/ICRA48891.2023.10160591","volume":"2023","author":"J Liang","year":"2023","unstructured":"Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2023). Code as policies: Language model programs for embodied control. IEEE International Conference on Robotics and Automation (ICRA), 2023, 9493\u20139500. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10160591","journal-title":"IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"2294_CR39","doi-asserted-by":"publisher","unstructured":"Liang, K.-H., Davidson, S., Yuan, X., Panditharatne, S., Chen, C.-Y., Shea, R., Pham, D., Tan, Y., Voss, E., & Fryer, L. (2023). ChatBack: Investigating methods of providing grammatical error feedback in a GUI-based language learning chatbot. In Proceedings of the 18th workshop on innovative use of NLP for building educational applications (BEA 2023) (pp. 83\u201399). https:\/\/doi.org\/10.18653\/v1\/2023.bea-1.7","DOI":"10.18653\/v1\/2023.bea-1.7"},{"key":"2294_CR40","unstructured":"Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning. arXiv:2304.08485"},{"key":"2294_CR41","doi-asserted-by":"crossref","unstructured":"Luketina, J., Nardelli, N., Farquhar, G., Foerster, J., Andreas, J., Grefenstette, E., Whiteson, S., & Rockt\u00e4schel, T. (2019). A survey of reinforcement learning informed by natural language. arXiv:1906.03926","DOI":"10.24963\/ijcai.2019\/880"},{"key":"2294_CR42","doi-asserted-by":"publisher","unstructured":"Misra, D., Langford, J., & Artzi, Y. (2017). Mapping instructions and visual observations to actions with reinforcement learning. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 1004\u20131015). https:\/\/doi.org\/10.18653\/v1\/D17-1106","DOI":"10.18653\/v1\/D17-1106"},{"key":"2294_CR43","first-page":"33947","volume":"35","author":"J Mu","year":"2022","unstructured":"Mu, J., Zhong, V., Raileanu, R., Jiang, M., Goodman, N., Rockt\u00e4schel, T., & Grefenstette, E. (2022). Improving intrinsic exploration with language abstractions. Advances in Neural Information Processing Systems, 35, 33947\u201333960.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2294_CR44","unstructured":"Nair, S., Mitchell, E., Chen, K., Ichter, B., Savarese, S., & Finn, C. (2022). Learning language-conditioned robot behavior from offline data and crowdsourced annotation. In Proceedings of the 5th conference on robot learning (Vol. 164, pp. 1303\u20131315)."},{"key":"2294_CR45","doi-asserted-by":"crossref","unstructured":"Nascimento, N., Alencar, P., & Cowan, D. (2023). Self-adaptive large language model (LLM)-based multiagent systems. arXiv:2307.06187","DOI":"10.1109\/ACSOS-C58168.2023.00048"},{"key":"2294_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02214-0","author":"C Neunzig","year":"2023","unstructured":"Neunzig, C., M\u00f6llensiep, D., Kuhlenk\u00f6tter, B., & M\u00f6ller, M. (2023). ML Pro: Digital assistance system for interactive machine learning in production. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-023-02214-0","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2294_CR47","unstructured":"OpenAI. (2023). GPT-4 technical report. arXiv:2303.08774"},{"issue":"10","key":"2294_CR48","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1177\/0278364918777627","volume":"37","author":"R Paul","year":"2018","unstructured":"Paul, R., Arkin, J., Aksaray, D., Roy, N., & Howard, T. M. (2018). Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms. The International Journal of Robotics Research, 37(10), 1269\u20131299. https:\/\/doi.org\/10.1177\/0278364918777627","journal-title":"The International Journal of Robotics Research"},{"key":"2294_CR49","unstructured":"Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023). Instruction tuning with GPT-4. arXiv:2304.03277"},{"key":"2294_CR50","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/IROS.2016.7759358","volume":"2016","author":"A Perzylo","year":"2016","unstructured":"Perzylo, A., Somani, N., Profanter, S., Kessler, I., Rickert, M., & Knoll, A. (2016). Intuitive instruction of industrial robots: Semantic process descriptions for small lot production. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 2293\u20132300. https:\/\/doi.org\/10.1109\/IROS.2016.7759358","journal-title":"IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"},{"key":"2294_CR51","doi-asserted-by":"crossref","unstructured":"Petroni, F., Rockt\u00e4schel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H., & Riedel, S. (2019). Language models as knowledge bases? arXiv:1909.01066","DOI":"10.18653\/v1\/D19-1250"},{"key":"2294_CR52","unstructured":"Poesia, G., Polozov, O., Le, V., Tiwari, A., Soares, G., Meek, C., & Gulwani, S. (2022). Synchromesh: Reliable code generation from pre-trained language models. arXiv:2201.11227"},{"key":"2294_CR53","doi-asserted-by":"crossref","unstructured":"Raman, S. S., Cohen, V., Paulius, D., Idrees, I., Rosen, E., Mooney, R., & Tellex, S. (2023). Cape: Corrective actions from precondition errors using large language models. arXiv:2211.09935","DOI":"10.1109\/ICRA57147.2024.10611376"},{"key":"2294_CR54","unstructured":"Ren, P., Zhang, K., Zheng, H., Li, Z., Wen, Y., Zhu, F., Ma, M., & Liang, X. (2023). RM-PRT: Realistic robotic manipulation simulator and benchmark with progressive reasoning tasks. arXiv:2306.11335"},{"key":"2294_CR55","doi-asserted-by":"publisher","unstructured":"Rovida, F., Crosby, M., Holz, D., Polydoros, A. S., Gro\u00dfmann, B., Petrick, R. P. A., & Kr\u00fcger, V. (2017). SkiROS\u2014A skill-based robot control platform on top of ROS. Robot Operating System (ROS) The Complete Reference (Volume 2), 121\u2013160. https:\/\/doi.org\/10.1007\/978-3-319-54927-9_4","DOI":"10.1007\/978-3-319-54927-9_4"},{"key":"2294_CR56","unstructured":"Shah, D., Osi\u0144ski, B., Ichter, B., & Levine, S. (2023). LM-NAV: Robotic navigation with large pretrained models of language, vision, and action. In Proceedings of the 6th conference on robot learning (pp. 492\u2013504)."},{"key":"2294_CR57","doi-asserted-by":"crossref","unstructured":"Sharma, P., Sundaralingam, B., Blukis, V., Paxton, C., Hermans, T., Torralba, A., Andreas, J., & Fox, D. (2022). Correcting robot plans with natural language feedback. arXiv:2204.05186","DOI":"10.15607\/RSS.2022.XVIII.065"},{"key":"2294_CR58","doi-asserted-by":"publisher","first-page":"11523","DOI":"10.1109\/ICRA48891.2023.10161317","volume":"2023","author":"I Singh","year":"2023","unstructured":"Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2023). ProgPrompt: Generating situated robot task plans using large language models. IEEE International Conference on Robotics and Automation (ICRA), 2023, 11523\u201311530. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10161317","journal-title":"IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"2294_CR59","doi-asserted-by":"crossref","unstructured":"Tellex, S., Kollar, T., Dickerson, S., Walter, M., Banerjee, A., Teller, S., & Roy, N. (2011). Understanding natural language commands for robotic navigation and mobile manipulation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 25, pp. 1507\u20131514).","DOI":"10.1609\/aaai.v25i1.7979"},{"key":"2294_CR60","doi-asserted-by":"publisher","unstructured":"Thomason, J., Zhang, S., Mooney, R., & Stone, P. (2015). Learning to interpret natural language commands through human-robot dialog. In Proceedings of the 24th international conference on artificial intelligence (pp. 1923\u20131929). https:\/\/doi.org\/10.5555\/2832415.2832516","DOI":"10.5555\/2832415.2832516"},{"key":"2294_CR61","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., ..., Scialom, T. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288"},{"key":"2294_CR62","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.robot.2017.10.012","volume":"99","author":"M W\u00e4chter","year":"2018","unstructured":"W\u00e4chter, M., Ovchinnikova, E., Wittenbeck, V., Kaiser, P., Szedmak, S., Mustafa, W., Kraft, D., Kr\u00fcger, N., Piater, J., & Asfour, T. (2018). Integrating multi-purpose natural language understanding, robot\u2019s memory, and symbolic planning for task execution in humanoid robots. Robotics and Autonomous Systems, 99, 148\u2013165. https:\/\/doi.org\/10.1016\/j.robot.2017.10.012","journal-title":"Robotics and Autonomous Systems"},{"key":"2294_CR63","unstructured":"Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent abilities of large language models. arXiv:2206.07682"},{"key":"2294_CR64","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2023). Chain-of-thought prompting elicits reasoning in large language models. arXiv:2201.11903"},{"key":"2294_CR65","unstructured":"Yang, Y., Zhang, X., & Han, W. (2023). Enhance reasoning ability of visual-language models via large language models. arXiv:2305.13267"},{"key":"2294_CR66","unstructured":"Ye, J., Chen, X., Xu, N., Zu, C., Shao, Z., Liu, S., Cui, Y., Zhou, Z., Gong, C., Shen, Y., Zhou, J., Chen, S., Gui, T., Zhang, Q., & Huang, X. (2023). A comprehensive capability analysis of GPT-3 and GPT-3.5 series models. arXiv:2303.10420"},{"key":"2294_CR67","doi-asserted-by":"crossref","unstructured":"Yoneda, T., Fang, J., Li, P., Zhang, H., Jiang, T., Lin, S., Picker, B., Yunis, D., Mei, H., & Walter, M. R. (2023). Statler: State-maintaining language models for embodied reasoning. arXiv:2306.17840","DOI":"10.1109\/ICRA57147.2024.10610634"},{"key":"2294_CR68","unstructured":"Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., & Florence, P. (2022). Socratic models: Composing zero-shot multimodal reasoning with language. arXiv:2204.00598"},{"key":"2294_CR69","unstructured":"Zhang, D., Chen, L., Zhao, Z., Cao, R., & Yu, K. (2023). Mobile-Env: An evaluation platform and benchmark for interactive agents in LLM era. arXiv:2305.08144"},{"key":"2294_CR70","unstructured":"Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., ..., Wen, J.-R. (2023). A survey of large language models. arXiv:2303.18223"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02294-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02294-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02294-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T22:29:48Z","timestamp":1738621788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02294-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":70,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2294"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02294-y","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"1 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}