{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:25:11Z","timestamp":1775665511299,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100015599","name":"Toyota Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015599","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton Robot"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10514-023-10131-7","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T08:02:49Z","timestamp":1699948969000},"page":"1345-1365","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":176,"title":["Text2Motion: from natural language instructions to feasible plans"],"prefix":"10.1007","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1346-1119","authenticated-orcid":false,"given":"Kevin","family":"Lin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1208-2539","authenticated-orcid":false,"given":"Christopher","family":"Agia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1106-4152","authenticated-orcid":false,"given":"Toki","family":"Migimatsu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-4337","authenticated-orcid":false,"given":"Marco","family":"Pavone","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4921-7193","authenticated-orcid":false,"given":"Jeannette","family":"Bohg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"10131_CR1","unstructured":"Aeronautiques, C., Howe, A., Knoblock, C., McDermott, I. D., Ram, A., Veloso, M., Weld, D., SRI, D. W., Barrett, A., Christianson, D., et al. (1998). PDDL| the planning domain definition language. Technical Report."},{"key":"10131_CR2","doi-asserted-by":"crossref","unstructured":"Agia, C., Migimatsu, T., Wu, J., & Bohg, J. (2022). STAP: Sequencing task-agnostic policies. arXiv preprint arXiv:2210.12250","DOI":"10.1109\/ICRA48891.2023.10160220"},{"key":"10131_CR3","unstructured":"Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., et al. (2022). Do as i can, not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691"},{"key":"10131_CR4","doi-asserted-by":"crossref","unstructured":"Ames, B., Thackston, A., & Konidaris, G. (2018). Learning symbolic representations for planning with parameterized skills. In 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 526\u2013533). IEEE.","DOI":"10.1109\/IROS.2018.8594313"},{"key":"10131_CR5","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.artint.2015.03.005","volume":"247","author":"J Bidot","year":"2017","unstructured":"Bidot, J., Karlsson, L., Lagriffoul, F., & Saffiotti, A. (2017). Geometric backtracking for combined task and motion planning in robotic systems. Artificial Intelligence, 247, 229\u2013265.","journal-title":"Artificial Intelligence"},{"key":"10131_CR6","unstructured":"Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258"},{"issue":"1\u20132","key":"10131_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0004-3702(01)00108-4","volume":"129","author":"B Bonet","year":"2001","unstructured":"Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1\u20132), 5\u201333.","journal-title":"Artificial Intelligence"},{"key":"10131_CR8","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., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, K.-H., Levine, S., Lu, Y., Malla, U., Manjunath, D., Mordatch, I., Nachum, O., Parada, C., Peralta, J., Perez, E., Pertsch, K., Quiambao, J., Rao, K., Ryoo, M., Salazar, G., Sanketi, P., Sayed, K., Singh, J., Sontakke, S., Stone, A., Tan, C., Tran, H., Vanhoucke, V., Vega, S., Vuong, Q., Xia, F., Xiao, T., Xu, P., Xu, S., Yu, T., & Zitkovich, B. (2022). Rt-1: Robotics transformer for real-world control at scale. In: arXiv Preprint arXiv:2212.06817","DOI":"10.15607\/RSS.2023.XIX.025"},{"key":"10131_CR9","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10131_CR10","doi-asserted-by":"crossref","unstructured":"Chen, B., Xia, F., Ichter, B., Rao, K., Gopalakrishnan, K., Ryoo, M.S., Stone, A., & Kappler, D. (2022a). Open-vocabulary queryable scene representations for real world planning. arXiv preprint arXiv:2209.09874","DOI":"10.1109\/ICRA48891.2023.10161534"},{"key":"10131_CR11","unstructured":"Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374"},{"key":"10131_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yuan, L., Cui, G., Liu, Z., & Ji, H. (2022b). A close look into the calibration of pre-trained language models. arXiv preprint arXiv:2211.00151","DOI":"10.18653\/v1\/2023.acl-long.75"},{"key":"10131_CR13","unstructured":"Chitnis, R., Silver, T., Kim, B., Kaelbling, L., & Lozano-Perez, T. (2021). Camps: Learning context-specific abstractions for efficient planning in factored MDPS. In Conference on robot learning (pp. 64\u201379). PMLR."},{"key":"10131_CR14","doi-asserted-by":"crossref","unstructured":"Chitnis, R., Silver, T., Tenenbaum, J. B., Lozano-Perez, T., & Kaelbling, L. P. (2022). Learning neuro-symbolic relational transition models for bilevel planning. In 2022 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 4166\u20134173). IEEE.","DOI":"10.1109\/IROS47612.2022.9981440"},{"key":"10131_CR15","doi-asserted-by":"crossref","unstructured":"Curtis, A., Fang, X., Kaelbling, L. P., Lozano-P\u00e9rez, T., & Garrett, C. R. (2022). Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 1940\u20131946). IEEE.","DOI":"10.1109\/ICRA46639.2022.9812057"},{"key":"10131_CR16","doi-asserted-by":"crossref","unstructured":"Curtis, A., Silver, T., Tenenbaum, J. B., Lozano-P\u00e9rez, T., & Kaelbling, L. (2022). Discovering state and action abstractions for generalized task and motion planning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, pp. 5377\u20135384).","DOI":"10.1609\/aaai.v36i5.20475"},{"key":"10131_CR17","unstructured":"Dalal, M., Mandlekar, A., Garrett, C., Handa, A., Salakhutdinov, R., & Fox, D. (2023). Imitating task and motion planning with visuomotor transformers. arXiv preprint arXiv:2305.16309"},{"key":"10131_CR18","doi-asserted-by":"publisher","unstructured":"Dantam, N. T., Kingston, Z. K., Chaudhuri, S., & Kavraki, L. E. (2016). Incremental task and motion planning: A constraint-based approach. In Robotics: Science and systems, Ann Arbor, Michigan. https:\/\/doi.org\/10.15607\/RSS.2016.XII.002","DOI":"10.15607\/RSS.2016.XII.002"},{"key":"10131_CR19","doi-asserted-by":"crossref","unstructured":"Driess, D., Ha, J.-S., & Toussaint, M. (2020a). Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image. arXiv preprint arXiv:2006.05398","DOI":"10.15607\/RSS.2020.XVI.003"},{"issue":"12\u201314","key":"10131_CR20","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1177\/02783649211056967","volume":"40","author":"D Driess","year":"2021","unstructured":"Driess, D., Ha, J.-S., & Toussaint, M. (2021). Learning to solve sequential physical reasoning problems from a scene image. The International Journal of Robotics Research, 40(12\u201314), 1435\u20131466.","journal-title":"The International Journal of Robotics Research"},{"key":"10131_CR21","doi-asserted-by":"crossref","unstructured":"Driess, D., Ha, J.-S., Tedrake, R., & Toussaint, M. (2021b). Learning geometric reasoning and control for long-horizon tasks from visual input. In 2021 IEEE international conference on robotics and automation (ICRA) (pp. 14298\u201314305). IEEE.","DOI":"10.1109\/ICRA48506.2021.9560934"},{"key":"10131_CR22","unstructured":"Driess, D., Huang, Z., Li, Y., Tedrake, R., & Toussaint, M. (2023). Learning multi-object dynamics with compositional neural radiance fields. In Proceedings of the 6th conference on robot learning. Proceedings of machine learning research (Vol. 205, pp. 1755\u20131768). PMLR."},{"key":"10131_CR23","doi-asserted-by":"crossref","unstructured":"Driess, D., Oguz, O., Ha, J.-S., Toussaint, M. (2020b). Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning. In 2020 IEEE international conference on robotics and automation (ICRA) (pp. 9563\u20139569). IEEE.","DOI":"10.1109\/ICRA40945.2020.9197291"},{"key":"10131_CR24","unstructured":"Driess, D., Oguz, O., Toussaint, M. (2019). Hierarchical task and motion planning using logic-geometric programming (HLGP). In RSS workshop on robust task and motion planning."},{"key":"10131_CR25","unstructured":"Driess, D., Xia, F., Sajjadi, M.S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023). Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378"},{"issue":"3","key":"10131_CR26","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.robot.2012.11.010","volume":"61","author":"J Felip","year":"2013","unstructured":"Felip, J., Laaksonen, J., Morales, A., & Kyrki, V. (2013). Manipulation primitives: A paradigm for abstraction and execution of grasping and manipulation tasks. Robotics and Autonomous Systems, 61(3), 283\u2013296.","journal-title":"Robotics and Autonomous Systems"},{"key":"10131_CR27","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1146\/annurev-control-091420-084139","volume":"4","author":"CR Garrett","year":"2021","unstructured":"Garrett, C. R., Chitnis, R., Holladay, R., Kim, B., Silver, T., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2021). Integrated task and motion planning. Annual Review of Control, Robotics, and Autonomous Systems, 4, 265\u2013293.","journal-title":"Annual Review of Control, Robotics, and Autonomous Systems"},{"key":"10131_CR28","doi-asserted-by":"crossref","unstructured":"Garrett, C. R., Lozano-P\u00e9rez, T., & Kaelbling, L. P. (2020). Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning. In Proceedings of the international conference on automated planning and scheduling (Vol. 30, pp. 440\u2013448).","DOI":"10.1609\/icaps.v30i1.6739"},{"key":"10131_CR29","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In J. Dy, & A. Krause (Eds.), Proceedings of the 35th international conference on machine learning. Proceedings of machine learning research (Vol. 80, pp. 1861\u20131870). PMLR. https:\/\/proceedings.mlr.press\/v80\/haarnoja18b.html"},{"key":"10131_CR30","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1613\/jair.1705","volume":"26","author":"M Helmert","year":"2006","unstructured":"Helmert, M. (2006). The fast downward planning system. Journal of Artificial Intelligence Research, 26, 191\u2013246.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10131_CR31","unstructured":"Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022). Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. arXiv preprint arXiv:2201.07207"},{"key":"10131_CR32","unstructured":"Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022). Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608"},{"key":"10131_CR33","unstructured":"Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., & Finn, C. (2021). BC-z: Zero-shot task generalization with robotic imitation learning. In 5th annual conference on robot learning. https:\/\/openreview.net\/forum?id=8kbp23tSGYv"},{"key":"10131_CR34","unstructured":"Jiang, Y., Gupta, A., Zhang, Z., Wang, G., Dou, Y., Chen, Y., Fei-Fei, L., Anandkumar, A., Zhu, Y., & Fan, L. (2022). Vima: General robot manipulation with multimodal prompts. arXiv preprint arXiv:2210.03094"},{"key":"10131_CR35","doi-asserted-by":"publisher","unstructured":"Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2011). Hierarchical task and motion planning in the now. In 2011 IEEE international conference on robotics and automation (pp. 1470\u20131477). https:\/\/doi.org\/10.1109\/ICRA.2011.5980391","DOI":"10.1109\/ICRA.2011.5980391"},{"key":"10131_CR36","doi-asserted-by":"crossref","unstructured":"Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2012). Integrated robot task and motion planning in the now. Technical report: Massachusetts Inst of Tech Cambridge Computer Science and Artificial.","DOI":"10.21236\/ADA564092"},{"key":"10131_CR37","unstructured":"Kalashnkov, D., Varley, J., Chebotar, Y., Swanson, B., Jonschkowski, R., Finn, C., Levine, S., & Hausman, K. (2021). Mt-opt: Continuous multi-task robotic reinforcement learning at scale. arXiv"},{"issue":"1","key":"10131_CR38","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JRA.1987.1087068","volume":"3","author":"O Khatib","year":"1987","unstructured":"Khatib, O. (1987). A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal on Robotics and Automation, 3(1), 43\u201353.","journal-title":"IEEE Journal on Robotics and Automation"},{"key":"10131_CR39","doi-asserted-by":"crossref","unstructured":"Kim, B., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2019). Adversarial actor-critic method for task and motion planning problems using planning experience. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 8017\u20138024).","DOI":"10.1609\/aaai.v33i01.33018017"},{"issue":"2","key":"10131_CR40","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1177\/02783649211038280","volume":"41","author":"B Kim","year":"2022","unstructured":"Kim, B., Shimanuki, L., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2022). Representation, learning, and planning algorithms for geometric task and motion planning. The International Journal of Robotics Research, 41(2), 210\u2013231.","journal-title":"The International Journal of Robotics Research"},{"key":"10131_CR41","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1613\/jair.5575","volume":"61","author":"G Konidaris","year":"2018","unstructured":"Konidaris, G., Kaelbling, L. P., & Lozano-Perez, T. (2018). From skills to symbols: Learning symbolic representations for abstract high-level planning. Journal of Artificial Intelligence Research, 61, 215\u2013289.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10131_CR42","doi-asserted-by":"crossref","unstructured":"Kroemer, O., & Sukhatme, G. S. (2016). Learning spatial preconditions of manipulation skills using random forests. In 2016 IEEE-RAS 16th international conference on humanoid robots (Humanoids) (pp. 676\u2013683). IEEE.","DOI":"10.1109\/HUMANOIDS.2016.7803347"},{"issue":"14","key":"10131_CR43","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1177\/0278364914545811","volume":"33","author":"F Lagriffoul","year":"2014","unstructured":"Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A., & Karlsson, L. (2014). Efficiently combining task and motion planning using geometric constraints. The International Journal of Robotics Research, 33(14), 1726\u20131747.","journal-title":"The International Journal of Robotics Research"},{"key":"10131_CR44","unstructured":"Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in neural information processing systems (Vol. 30)."},{"key":"10131_CR45","doi-asserted-by":"crossref","unstructured":"Li, X.L., Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., Zettlemoyer, L., & Lewis, M. (2022). Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097","DOI":"10.18653\/v1\/2023.acl-long.687"},{"key":"10131_CR46","doi-asserted-by":"crossref","unstructured":"Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2022). Code as policies: Language model programs for embodied control. arXiv preprint arXiv:2209.07753","DOI":"10.1109\/ICRA48891.2023.10160591"},{"key":"10131_CR47","unstructured":"Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., & Stone, P. (2023). Llm+ p: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477"},{"key":"10131_CR48","unstructured":"Loshchilov, I., & Hutter, F. (2017). SGDR: Stochastic gradient descent with warm restarts. In International conference on learning representations. https:\/\/openreview.net\/forum?id=Skq89Scxx"},{"issue":"3","key":"10131_CR49","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., & Burgard, W. (2022). Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters (RA-L), 7(3), 7327\u20137334.","journal-title":"IEEE Robotics and Automation Letters (RA-L)"},{"key":"10131_CR50","unstructured":"OpenAI: GPT-4 Technical Report (2023)"},{"key":"10131_CR51","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022). Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155"},{"issue":"2","key":"10131_CR52","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1023\/A:1010091220143","volume":"1","author":"R Rubinstein","year":"1999","unstructured":"Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and Computing in Applied Probability, 1(2), 127\u2013190.","journal-title":"Methodology and Computing in Applied Probability"},{"issue":"12\u201314","key":"10131_CR53","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1177\/02783649211046285","volume":"40","author":"L Shao","year":"2021","unstructured":"Shao, L., Migimatsu, T., Zhang, Q., Yang, K., & Bohg, J. (2021). Concept2robot: Learning manipulation concepts from instructions and human demonstrations. The International Journal of Robotics Research, 40(12\u201314), 1419\u20131434.","journal-title":"The International Journal of Robotics Research"},{"key":"10131_CR54","unstructured":"Shridhar, M., Manuelli, L., & Fox, D. (2022a). Cliport: What and where pathways for robotic manipulation. In: Conference on robot learning (pp. 894\u2013906). PMLR."},{"key":"10131_CR55","unstructured":"Shridhar, M., Manuelli, L., & Fox, D. (2022b). Perceiver-actor: A multi-task transformer for robotic manipulation. arXiv preprint arXiv:2209.05451"},{"key":"10131_CR56","unstructured":"Silver, T., Athalye, A., Tenenbaum, J. B., Lozano-P\u00e9rez, T., & Kaelbling, L. P. (2022). Learning neuro-symbolic skills for bilevel planning. In 6th annual conference on robot learning. https:\/\/openreview.net\/forum?id=OIaJRUo5UXy."},{"key":"10131_CR57","doi-asserted-by":"crossref","unstructured":"Silver, T., Chitnis, R., Curtis, A., Tenenbaum, J. B., Lozano-Perez, T., & Kaelbling, L. P. (2021). Planning with learned object importance in large problem instances using graph neural networks. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, pp. 11962\u201311971).","DOI":"10.1609\/aaai.v35i13.17421"},{"key":"10131_CR58","doi-asserted-by":"crossref","unstructured":"Silver, T., Chitnis, R., Tenenbaum, J., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2021). Learning symbolic operators for task and motion planning. In 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 3182\u20133189). IEEE.","DOI":"10.1109\/IROS51168.2021.9635941"},{"key":"10131_CR59","unstructured":"Silver, T., Hariprasad, V., Shuttleworth, R. S., Kumar, N., Lozano-P\u00e9rez, T., & Kaelbling, L. P. (2022). PDDL planning with pretrained large language models. In NeurIPS 2022 foundation models for decision making workshop."},{"key":"10131_CR60","doi-asserted-by":"crossref","unstructured":"Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2022). Progprompt: Generating situated robot task plans using large language models. arXiv preprint arXiv:2209.11302","DOI":"10.1007\/s10514-023-10135-3"},{"key":"10131_CR61","unstructured":"Skreta, M., Yoshikawa, N., Arellano-Rubach, S., Ji, Z., Kristensen, L.B., Darvish, K., Aspuru-Guzik, A., Shkurti, F., & Garg, A. (2023). Errors are useful prompts: Instruction guided task programming with verifier-assisted iterative prompting. arXiv preprint arXiv:2303.14100"},{"key":"10131_CR62","first-page":"13139","volume":"33","author":"S Stepputtis","year":"2020","unstructured":"Stepputtis, S., Campbell, J., Phielipp, M., Lee, S., Baral, C., & Ben Amor, H. (2020). Language-conditioned imitation learning for robot manipulation tasks. Advances in Neural Information Processing Systems, 33, 13139\u201313150.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10131_CR63","unstructured":"Toussaint, M. (2015). Logic-geometric programming: An optimization-based approach to combined task and motion planning. In Twenty-fourth international joint conference on artificial intelligence."},{"key":"10131_CR64","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"10131_CR65","unstructured":"Valmeekam, K., Olmo, A., Sreedharan, S., & Kambhampati, S. (2022). Large language models still can\u2019t plan (a benchmark for LLMS on planning and reasoning about change). arXiv preprint arXiv:2206.10498"},{"key":"10131_CR66","unstructured":"Vemprala, S., Bonatti, R., Bucker, A., & Kapoor, A. (2023). Chatgpt for robotics: Design principles and model abilities. Technical Report MSR-TR-2023-8, Microsoft"},{"key":"10131_CR67","unstructured":"Wang, Z., Cai, S., Liu, A., Ma, X., & Liang, Y. (2023). Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560"},{"key":"10131_CR68","doi-asserted-by":"crossref","unstructured":"Wang, Z., Garrett, C. R., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2018). Active model learning and diverse action sampling for task and motion planning. In 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 4107\u20134114). IEEE.","DOI":"10.1109\/IROS.2018.8594027"},{"issue":"6\u20137","key":"10131_CR69","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1177\/02783649211004615","volume":"40","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Garrett, C. R., Kaelbling, L. P., & Lozano-P\u00e9rez, T. (2021). Learning compositional models of robot skills for task and motion planning. The International Journal of Robotics Research, 40(6\u20137), 866\u2013894.","journal-title":"The International Journal of Robotics Research"},{"key":"10131_CR70","unstructured":"Williams, G., Aldrich, A., & Theodorou, E. (2015). Model predictive path integral control using covariance variable importance sampling. arXiv preprint arXiv:1509.01149"},{"key":"10131_CR71","doi-asserted-by":"crossref","unstructured":"Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., & Funkhouser, T. (2023). Tidybot: Personalized robot assistance with large language models. arXiv preprint arXiv:2305.05658","DOI":"10.1007\/s10514-023-10139-z"},{"key":"10131_CR72","doi-asserted-by":"crossref","unstructured":"Xu, D., Mandlekar, A., Mart\u00edn-Mart\u00edn, R., Zhu, Y., Savarese, S., & Fei-Fei, L. (2021). Deep affordance foresight: Planning through what can be done in the future. In 2021 IEEE international conference on robotics and automation (ICRA) (pp. 6206\u20136213). IEEE.","DOI":"10.1109\/ICRA48506.2021.9560841"},{"key":"10131_CR73","unstructured":"Zelikman, E., Huang, Q., Poesia, G., Goodman, N. D., & Haber, N. (2022). Parsel: A unified natural language framework for algorithmic reasoning. arXiv preprint arXiv:2212.10561"},{"key":"10131_CR74","unstructured":"Zeng, A., Wong, A., Welker, S., Choromanski, K., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., et al. (2022). Socratic models: Composing zero-shot multimodal reasoning with language. arXiv preprint arXiv:2204.00598"},{"key":"10131_CR75","unstructured":"Zhao, Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate before use: Improving few-shot performance of language models. In International conference on machine learning (pp. 12697\u201312706). PMLR."},{"key":"10131_CR76","doi-asserted-by":"crossref","unstructured":"Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020). Unified vision-language pre-training for image captioning and VQA. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, pp. 13041\u201313049).","DOI":"10.1609\/aaai.v34i07.7005"}],"container-title":["Autonomous Robots"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10131-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10514-023-10131-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10131-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T18:17:35Z","timestamp":1701195455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10514-023-10131-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"references-count":76,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10131"],"URL":"https:\/\/doi.org\/10.1007\/s10514-023-10131-7","relation":{},"ISSN":["0929-5593","1573-7527"],"issn-type":[{"value":"0929-5593","type":"print"},{"value":"1573-7527","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,14]]},"assertion":[{"value":"2 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute\/organization where the work has been carried out.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}