{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:24:57Z","timestamp":1772119497669,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Foundation","award":["CNS-1932068"],"award-info":[{"award-number":["CNS-1932068"]}]},{"name":"National Science Foundation","award":["CNS-1932068"],"award-info":[{"award-number":["CNS-1932068"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton Robot"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Training language-conditioned policies is typically time-consuming and resource-intensive. Additionally, the resulting controllers are tailored to the specific robot they were trained on, making it difficult to transfer them to other robots with different dynamics. To address these challenges, we propose a new approach called Hierarchical Modularity, which enables more efficient training and subsequent transfer of such policies across different types of robots. The approach incorporates Supervised Attention which bridges the gap between modular and end-to-end learning by enabling the re-use of functional building blocks. In this contribution, we build upon our previous work, showcasing the extended utilities and improved performance by expanding the hierarchy to include new tasks and introducing an automated pipeline for synthesizing a large quantity of novel objects. We demonstrate the effectiveness of this approach through extensive simulated and real-world robot manipulation experiments.<\/jats:p>","DOI":"10.1007\/s10514-023-10129-1","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T14:02:27Z","timestamp":1693404147000},"page":"1013-1033","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning modular language-conditioned robot policies through attention"],"prefix":"10.1007","volume":"47","author":[{"given":"Yifan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shubham","family":"Sonawani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariano","family":"Phielipp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heni","family":"Ben\u00a0Amor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Stepputtis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"10129_CR1","doi-asserted-by":"crossref","unstructured":"Abolghasemi, P., Mazaheri, A., Shah, M., et\u00a0al. (2019). Pay attention!-robustifying a deep visuomotor policy through task-focused visual attention. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4254\u20134262).","DOI":"10.1109\/CVPR.2019.00438"},{"key":"10129_CR2","unstructured":"Ahn, M., Brohan, A., Brown, N., et\u00a0al. (2022). Do as I can, not as I say: Grounding language in robotic affordances. arXiv:2204.01691"},{"key":"10129_CR3","first-page":"23716","volume":"35","author":"JB Alayrac","year":"2022","unstructured":"Alayrac, J. B., Donahue, J., Luc, P., et al. (2022). Flamingo: A visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35, 23716\u201323736.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10129_CR4","unstructured":"Anderson, P., Shrivastava, A., Parikh, D., et\u00a0al. (2019). Chasing ghosts: Instruction following as Bayesian state tracking. In Advances in neural information processing systems (Vol. 32)."},{"key":"10129_CR5","doi-asserted-by":"crossref","unstructured":"Antol, S., Agrawal, A., Lu, J., et\u00a0al. (2015). Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (pp. 2425\u20132433).","DOI":"10.1109\/ICCV.2015.279"},{"issue":"5","key":"10129_CR6","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.robot.2008.10.024","volume":"57","author":"BD Argall","year":"2009","unstructured":"Argall, B. D., Chernova, S., Veloso, M., et al. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469\u2013483.","journal-title":"Robotics and Autonomous Systems"},{"key":"10129_CR7","unstructured":"Arkin, R. (1998). Behavior-based robotics. The MIT Press."},{"key":"10129_CR8","doi-asserted-by":"publisher","unstructured":"Bengio, Y., Louradour, J., Collobert, R., et\u00a0al. (2009). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning. Association for Computing Machinery, New York, NY, USA, ICML \u201909 (pp. 41\u201348). https:\/\/doi.org\/10.1145\/1553374.1553380","DOI":"10.1145\/1553374.1553380"},{"issue":"1","key":"10129_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/JRA.1986.1087032","volume":"2","author":"R Brooks","year":"1986","unstructured":"Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal on Robotics and Automation, 2(1), 14\u201323. https:\/\/doi.org\/10.1109\/JRA.1986.1087032","journal-title":"IEEE Journal on Robotics and Automation"},{"key":"10129_CR10","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., et\u00a0al. (2020). End-to-end object detection with transformers. In European conference on computer vision (pp. 213\u2013229). Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"10129_CR11","doi-asserted-by":"publisher","unstructured":"Chen, Y. C., Li, L., Yu, L., et\u00a0al. (2020). Uniter: Universal image-text representation learning. In: Computer vision\u2014ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXX (pp. 104\u2014120). Springer. https:\/\/doi.org\/10.1007\/978-3-030-58577-8_7","DOI":"10.1007\/978-3-030-58577-8_7"},{"issue":"7","key":"10129_CR12","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/1538788.1538812","volume":"52","author":"A Coates","year":"2009","unstructured":"Coates, A., Abbeel, P., & Ng, A. Y. (2009). Apprenticeship learning for helicopter control. Communications of the ACM, 52(7), 97\u2013105.","journal-title":"Communications of the ACM"},{"key":"10129_CR13","unstructured":"Csord\u00e1s, R., van Steenkiste, S., & Schmidhuber, J. (2021). Are neural nets modular? inspecting functional modularity through differentiable weight masks. In International conference on learning representations."},{"key":"10129_CR14","doi-asserted-by":"crossref","unstructured":"Das, A., Kottur, S., Gupta, K., et\u00a0al. (2017). Visual dialog. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 326\u2013335).","DOI":"10.1109\/CVPR.2017.121"},{"key":"10129_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT de Boer","year":"2004","unstructured":"de Boer, P. T., Kroese, D. P., Mannor, S., et al. (2004). A tutorial on the cross-entropy method. Annals of Operations Research, 134, 19\u201367.","journal-title":"Annals of Operations Research"},{"key":"10129_CR16","doi-asserted-by":"crossref","unstructured":"Dillmann, R., & Friedrich, H. (1996). Programming by demonstration: A machine learning approach to support skill acquision for robots. In International conference on artificial intelligence and symbolic mathematical computing (pp. 87\u2013108). Springer.","DOI":"10.1007\/3-540-61732-9_52"},{"key":"10129_CR17","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et\u00a0al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929."},{"key":"10129_CR18","unstructured":"Duan, Y., Andrychowicz, M., Stadie, B., et al. (2017a). One-shot imitation learning. In I. Guyon, U. V. Luxburg, S. Bengio, et al. (Eds.), Advances in Neural Information Processing Systems.  (Vol. 30). Curran Associates Inc."},{"key":"10129_CR19","unstructured":"Duan, Y., Andrychowicz, M., Stadie, B., et\u00a0al. (2017b). One-shot imitation learning. In Advances in neural information processing systems (Vol. 30)."},{"key":"10129_CR20","unstructured":"Filan, D., Hod, S., Wild, C., et\u00a0al. (2020). Neural networks are surprisingly modular. arXiv:2003.04881, cite Comment: 23 pages, 13 figures."},{"key":"10129_CR21","unstructured":"Huang, W., Xia, F., Xiao, T., et\u00a0al. (2022). Inner monologue: Embodied reasoning through planning with language models. arXiv:2207.05608"},{"key":"10129_CR22","unstructured":"Jang, E., Irpan, A., Khansari, M., et\u00a0al. (2022). Bc-z: Zero-shot task generalization with robotic imitation learning. In Conference on robot learning, PMLR (pp. 991\u20131002)."},{"key":"10129_CR23","doi-asserted-by":"crossref","unstructured":"Johnson, J., Hariharan, B., Van Der\u00a0Maaten, L., et\u00a0al. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901\u20132910).","DOI":"10.1109\/CVPR.2017.215"},{"key":"10129_CR24","doi-asserted-by":"crossref","unstructured":"Kamath, A., Singh, M., LeCun, Y., et\u00a0al. (2021). Mdetr\u2014modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 1780\u20131790).","DOI":"10.1109\/ICCV48922.2021.00180"},{"key":"10129_CR25","doi-asserted-by":"crossref","unstructured":"Khatib, O. (1986). The potential field approach and operational space formulation in robot control. In Adaptive and learning systems (pp. 367\u2013377). Springer.","DOI":"10.1007\/978-1-4757-1895-9_26"},{"key":"10129_CR26","doi-asserted-by":"crossref","unstructured":"Kottur, S., Moura, J. M., Parikh, D., et\u00a0al. (2018). Visual coreference resolution in visual dialog using neural module networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 153\u2013169).","DOI":"10.1007\/978-3-030-01267-0_10"},{"key":"10129_CR27","doi-asserted-by":"crossref","unstructured":"Kuo, Y. L., Katz, B., & Barbu, A. (2020). Deep compositional robotic planners that follow natural language commands. In 2020 IEEE international conference on robotics and automation (ICRA) (pp. 4906\u20134912). IEEE.","DOI":"10.1109\/ICRA40945.2020.9197464"},{"key":"10129_CR28","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., & Navab, N. (2019). Towards unsupervised image captioning with shared multimodal embeddings. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2019.00751"},{"key":"10129_CR29","unstructured":"Liu, L., Utiyama, M., Finch, A., et\u00a0al. (2016). Neural machine translation with supervised attention. In Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers. The COLING 2016 Organizing Committee, Osaka, Japan (pp. 3093\u20133102). https:\/\/aclanthology.org\/C16-1291"},{"key":"10129_CR30","first-page":"11525","volume":"33","author":"F Locatello","year":"2020","unstructured":"Locatello, F., Weissenborn, D., Unterthiner, T., et al. (2020). Object-centric learning with slot attention. Advances in Neural Information Processing Systems, 33, 11525\u201311538.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10129_CR31","unstructured":"Lu, J., Batra, D., Parikh, D., et\u00a0al. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (Vol. 32)."},{"key":"10129_CR32","doi-asserted-by":"crossref","unstructured":"Lynch, C., & Sermanet, P. (2021). Language conditioned imitation learning over unstructured data. In Proceedings of robotics: Science and systems.","DOI":"10.15607\/RSS.2021.XVII.047"},{"key":"10129_CR33","doi-asserted-by":"crossref","unstructured":"Maeda, G., Ewerton, M., Lioutikov, R., et\u00a0al. (2014). Learning interaction for collaborative tasks with probabilistic movement primitives. In 2014 IEEE-RAS international conference on humanoid robots (pp. 527\u2013534). IEEE.","DOI":"10.1109\/HUMANOIDS.2014.7041413"},{"issue":"3","key":"10129_CR34","doi-asserted-by":"publisher","first-page":"7327","DOI":"10.1109\/LRA.2022.3180108","volume":"7","author":"O Mees","year":"2022","unstructured":"Mees, O., Hermann, L., Rosete-Beas, E., et al. (2022). Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters, 7(3), 7327\u20137334.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10129_CR35","unstructured":"Nair, S., Rajeswaran, A., Kumar, V., et\u00a0al. (2022). R3m: A universal visual representation for robot manipulation. arXiv:2203.12601"},{"key":"10129_CR36","unstructured":"OpenAI. (2023). Gpt-4 technical report. arXiv:2303.08774"},{"key":"10129_CR37","unstructured":"Ouyang, L., Wu, J., Jiang, X., et\u00a0al. (2022). Training language models to follow instructions with human feedback. arXiv:2203.02155"},{"issue":"2","key":"10129_CR38","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.robot.2005.09.004","volume":"53","author":"O Pettersson","year":"2005","unstructured":"Pettersson, O. (2005). Execution monitoring in robotics: A survey. Robotics and Autonomous Systems, 53(2), 73\u201388.","journal-title":"Robotics and Autonomous Systems"},{"key":"10129_CR39","unstructured":"Radford, A., Kim, J. W., Hallacy, C., et\u00a0al. (2021). Learning transferable visual models from natural language supervision. arXiv:2103.00020"},{"key":"10129_CR40","doi-asserted-by":"crossref","unstructured":"Rahmatizadeh, R., Abolghasemi, P., B\u00f6l\u00f6ni, L. et\u00a0al. (2018). Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 3758\u20133765), IEEE.","DOI":"10.1109\/ICRA.2018.8461076"},{"issue":"3","key":"10129_CR41","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2022","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., et al. (2022). Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 1623\u20131637.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10129_CR42","unstructured":"Reed, S., Zolna, K., Parisotto, E., et\u00a0al. (2022). A generalist agent. arXiv:2205.06175"},{"key":"10129_CR43","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., et\u00a0al. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10684\u201310695).","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"6","key":"10129_CR44","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S1364-6613(99)01327-3","volume":"3","author":"S Schaal","year":"1999","unstructured":"Schaal, S. (1999). Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3(6), 233\u2013242.","journal-title":"Trends in Cognitive Sciences"},{"key":"10129_CR45","doi-asserted-by":"crossref","unstructured":"Schaal, S. (2006). Dynamic movement primitives\u2014A framework for motor control in humans and humanoid robotics. In Adaptive motion of animals and machines (pp. 261\u2013280). Springer.","DOI":"10.1007\/4-431-31381-8_23"},{"key":"10129_CR46","unstructured":"Shridhar, M., Manuelli, L., & Fox, D. (2021). Cliport: What and where pathways for robotic manipulation. arXiv:2109.12098"},{"key":"10129_CR47","doi-asserted-by":"crossref","unstructured":"Singh, A., Hu, R., Goswami, V., et\u00a0al. (2022). Flava: A foundational language and vision alignment model. arXiv:2112.04482","DOI":"10.1109\/CVPR52688.2022.01519"},{"key":"10129_CR48","doi-asserted-by":"publisher","unstructured":"Sorkine, O., Cohen-Or, D., Lipman, Y., et\u00a0al. (2004). Laplacian surface editing. In Proceedings of the 2004 Eurographics\/ACM SIGGRAPH symposium on geometry processing. association for computing machinery, New York, NY, USA, SGP \u201904 (pp. 175\u2013184). https:\/\/doi.org\/10.1145\/1057432.1057456","DOI":"10.1145\/1057432.1057456"},{"key":"10129_CR49","unstructured":"Stepputtis, S., Campbell, J., Phielipp, M., et\u00a0al. (2020). Language-conditioned imitation learning for robot manipulation tasks. arXiv:2010.12083"},{"key":"10129_CR50","doi-asserted-by":"publisher","unstructured":"Tan, H., & Bansal, M. (2019). Lxmert: Learning cross-modality encoder representations from transformers. In K. Inui, J. Jiang, V. Ng, et\u00a0al. (Eds.) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3\u20137, 2019. Association for Computational Linguistics (pp. 5099\u20135110). https:\/\/doi.org\/10.18653\/v1\/D19-1514","DOI":"10.18653\/v1\/D19-1514"},{"key":"10129_CR51","doi-asserted-by":"crossref","unstructured":"Todorov, E., Erez, T., & Tassa, Y. (2012). Mujoco: A physics engine for model-based control. In 2012 IEEE\/RSJ international conference on intelligent robots and systems (pp. 5026\u20135033). IEEE.","DOI":"10.1109\/IROS.2012.6386109"},{"key":"10129_CR52","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et\u00a0al. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998\u20136008)."},{"key":"10129_CR53","unstructured":"Vemprala, S., Bonatti, R., Bucker, A., et\u00a0al. (2023). Chatgpt for robotics: Design principles and model abilities. 2023."},{"key":"10129_CR54","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., et\u00a0al. (2015). Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"10129_CR55","doi-asserted-by":"crossref","unstructured":"Wang, Y., Mishra, S., Alipoormolabashi, P., et\u00a0al. (2022). Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks. In Proceedings of the 2022 conference on empirical methods in natural language processing (pp. 5085\u20135109).","DOI":"10.18653\/v1\/2022.emnlp-main.340"},{"key":"10129_CR56","unstructured":"Xie, F., Chowdhury, A., De\u00a0Paolis\u00a0Kaluza, M. C., et\u00a0al. (2020). Deep imitation learning for bimanual robotic manipulation. In: H. Larochelle, M. Ranzato, R. Hadsell, et\u00a0al. (Eds.) Advances in neural information processing systems (Vol.\u00a033, pp. 2327\u20132337). Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/18a010d2a9813e91907ce88cd9143fdf-Paper.pdf"},{"key":"10129_CR57","unstructured":"Xu, K., Ba, J., Kiros, R., et\u00a0al. (2015). Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning, PMLR (pp. 2048\u20132057)."},{"key":"10129_CR58","doi-asserted-by":"crossref","unstructured":"Yamaguchi, T., & Fainekos, G. (2021). Percemon: Online monitoring for perception systems. In Runtime verification: 21st international conference, RV 2021, virtual event, October 11\u201314, 2021, Proceedings (p. 297). Springer.","DOI":"10.1007\/978-3-030-88494-9_18"},{"key":"10129_CR59","doi-asserted-by":"publisher","unstructured":"Zhang, T., McCarthy, Z., Jow, O., et\u00a0al. (2018a). Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 5628\u20135635). https:\/\/doi.org\/10.1109\/ICRA.2018.8461249","DOI":"10.1109\/ICRA.2018.8461249"},{"key":"10129_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, T., McCarthy, Z., Jow, O., et\u00a0al. (2018b). Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 5628\u20135635). IEEE.","DOI":"10.1109\/ICRA.2018.8461249"},{"key":"10129_CR61","unstructured":"Zhou, Y., Sonawani, S., Phielipp, M., et\u00a0al. (2022). Modularity through attention: Efficient training and transfer of language-conditioned policies for robot manipulation. arXiv:2212.04573"},{"key":"10129_CR62","unstructured":"Zhu, D., Chen, J., Shen, X., et\u00a0al. (2023). Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv:2304.10592"},{"key":"10129_CR63","unstructured":"Zhu, Y., Wong, J., Mandlekar, A., et\u00a0al. (2020). robosuite: A modular simulation framework and benchmark for robot learning. arXiv:2009.12293"},{"key":"10129_CR64","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13760","author":"T Zirr","year":"2019","unstructured":"Zirr, T., & Ritschel, T. (2019). Distortion-free displacement mapping. Computer Graphics Forum. https:\/\/doi.org\/10.1111\/cgf.13760","journal-title":"Computer Graphics Forum"}],"container-title":["Autonomous Robots"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10129-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10514-023-10129-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10129-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T13:10:25Z","timestamp":1701177025000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10514-023-10129-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":64,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10129"],"URL":"https:\/\/doi.org\/10.1007\/s10514-023-10129-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2884630\/v1","asserted-by":"object"}]},"ISSN":["0929-5593","1573-7527"],"issn-type":[{"value":"0929-5593","type":"print"},{"value":"1573-7527","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,30]]},"assertion":[{"value":"2 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This manuscript expands upon our prior conference paper, \u201cModularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation\u201d by Zhou et al., previously published as part of the Conference on Robot Learning (CoRL) 2022 proceedings held in Auckland, New Zealand. Particularly the methodology described in Sects.\u00a0\n                      \n                      and\n                      \n                      have previously been published. Similarly, the results discussed in Sects.\u00a0\n                      \n                      and\n                      \n                      have been previously published in the above publication. We confirm that the manuscript, in its current form, has not been published elsewhere and is not under consideration by another journal. Further, we confirm that all authors have approved the manuscript and agree with its submission to the Autonomous Robots Special Issue on Large Language Models in Robotics.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}