{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:12:48Z","timestamp":1779300768274,"version":"3.51.4"},"reference-count":59,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"114","license":[{"start":{"date-parts":[[2027,5,13]],"date-time":"2027-05-13T00:00:00Z","timestamp":1810166400000},"content-version":"vor","delay-in-days":365,"URL":"https:\/\/www.science.org\/content\/page\/science-licenses-journal-article-reuse"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2546659"],"award-info":[{"award-number":["2546659"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["D22AP00156-00"],"award-info":[{"award-number":["D22AP00156-00"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2026,5,13]]},"abstract":"<jats:p>Humans are remarkably data efficient when adapting to previously unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies trained using reinforcement learning (RL), are highly specialized for single environments. Because of this overfitting, they are known to break down even under small differences like the simulation-to-reality gap and require system identification and retraining for even minimal changes to the system. Here, we present RAPTOR, a method for training a highly adaptive foundation policy for quadrotor control. Our method enables training a single, end-to-end neural network policy to control a wide variety of quadrotors. We tested 10 different real quadrotors, from 32 grams to 2.4 kilograms, that also differed in motor type (brushed versus brushless), frame type (soft versus rigid), propeller type (two, three, or four blades), and flight controller (PX4, Betaflight, Crazyflie, M5StampFly). We found that a tiny, three-layer policy with only 2084 parameters was sufficient for zero-shot adaptation to a wide variety of platforms. The adaptation through in-context learning was made possible by using a recurrence in the hidden layer. The policy was trained through our proposed meta-imitation learning algorithm, where we sampled 1000 quadrotors and trained a teacher policy for each of them using RL. The 1000 teachers were distilled into a single, adaptive student policy. We found that within milliseconds, the resulting foundation policy adapted zero-shot to unseen quadrotors. We tested the capabilities of the foundation policy under numerous conditions (trajectory tracking, indoor\/outdoor, wind disturbance, poking, and different propellers).<\/jats:p>","DOI":"10.1126\/scirobotics.aec1481","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:58:15Z","timestamp":1778695095000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":0,"title":["RAPTOR: A foundation policy for quadrotor control"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-6502","authenticated-orcid":true,"given":"Jonas","family":"Eschmann","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences (EECS), UC Berkeley, Berkeley, CA 94720, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dario","family":"Albani","sequence":"additional","affiliation":[{"name":"Autonomous Robotics Research Center, Technology Innovation Institute, Abu Dhabi, UAE."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Loianno","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences (EECS), UC Berkeley, Berkeley, CA 94720, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2024.3502508"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2018.2852789"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abp9742"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adg1462"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06419-4"},{"key":"e_1_3_3_7_2","doi-asserted-by":"crossref","unstructured":"R. Ferede T. Blaha E. Lucassen C. De Wagter G. C. de Croon One net to rule them all: Domain randomization in quadcopter racing across different platforms. arXiv:2504.21586 [cs.RO] (2025).","DOI":"10.1109\/ICRA55743.2025.11128790"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3396025"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","unstructured":"X. B. Peng M. Andrychowicz W. Zaremba P. Abbeel \u201cSim-to-real transfer of robotic control with dynamics randomization\u201d in IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2018) pp. 3803\u20133810.","DOI":"10.1109\/ICRA.2018.8460528"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2019.2942989"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2024.3400838"},{"key":"e_1_3_3_12_2","unstructured":"A. Radford J. W. Kim C. Hallacy A. Ramesh G. Goh S. Agarwal G. Sastry A. Askell P. Mishkin J. Clark G. Krueger I. Sutskever \u201cLearning transferable visual models from natural language supervision\u201d in Proceedings of the 38th International Conference on Machine Learning M. Meila T. Zhang Eds. vol. 139 of Proceedings of Machine Learning Research (PMLR 2021) pp. 8748\u20138763."},{"key":"e_1_3_3_13_2","unstructured":"T. Brown B. Mann N. Ryder M. Subbiah J. Kaplan P. Dhariwal A. Neelakantan P. Shyam G. Sastry A. Askell S. Agarwal A. Herbert-Voss G. Krueger T. Henighan R. Child A. Ramesh D. M. Ziegler J. Wu C. Winter C. Hesse M. Chen E. Sigler M. Litwin S. Gray B. Chess J. Clark C. Berner S. M. Candlish A. Radford I. Sutskever D. Amodei \u201cLanguage models are few-shot learners\u201d in 34th Conference on Neural Information Processing Systems H. Larochelle M. Ranzato R. Hadsell M. F. Balcan H. Lin Eds. (Curran Associates Inc. 2020) pp. 1877\u20131901."},{"key":"e_1_3_3_14_2","unstructured":"J. Kaplan S. M. Candlish T. Henighan T. B. Brown B. Chess R. Child S. Gray A. Radford J. Wu D. Amodei Scaling laws for neural language models. arXiv:2001.08361 [cs.LG] (2020)."},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","unstructured":"E. Kaufmann L. Bauersfeld D. Scaramuzza \u201cA benchmark comparison of learned control policies for agile quadrotor flight\u201d in International Conference on Robotics and Automation (ICRA) (IEEE 2022) pp. 10504\u201310510.","DOI":"10.1109\/ICRA46639.2022.9811564"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","unstructured":"R. Zhang D. Zhang M. W. Mueller Proxfly: Robust control for close proximity quadcopter flight via residual reinforcement learning. arXiv:2409.13193 [cs.RO] (2024).","DOI":"10.1109\/ICRA55743.2025.11127714"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"J. Heeg Y. Song D. Scaramuzza Learning quadrotor control from visual features using differentiable simulation. arXiv:2410.15979 [cs.RO] (2024).","DOI":"10.1109\/ICRA55743.2025.11128641"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3520894"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","unstructured":"S. Gronauer M. Kissel L. Sacchetto M. Korte K. Diepold \u201cUsing simulation optimization to improve zero-shot policy transfer of quadrotors\u201d in 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2022) pp. 10170\u201310176.","DOI":"10.1109\/IROS47612.2022.9981229"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2023.104588"},{"key":"e_1_3_3_21_2","doi-asserted-by":"crossref","unstructured":"R. Ferede C. De Wagter D. Izzo G. C. De Croon \u201cEnd-to-end reinforcement learning for time-optimal quadcopter flight\u201d in 2024 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 6172\u20136177.","DOI":"10.1109\/ICRA57147.2024.10611665"},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","unstructured":"L. Balandi P. Robuffo Giordano M. Tognon \u201cAcceleration-based inner-loop control and MPC for aerial robots: Advantages and drawbacks\u201d in European Robotics Forum (Springer 2025) pp. 75\u201380.","DOI":"10.1007\/978-3-031-89471-8_12"},{"key":"e_1_3_3_23_2","unstructured":"S. M. Hegre W. Rehberg M. Kulkarni K. Alexis A neural network mode for PX4 on embedded flight controllers. arXiv:2505.00432 [cs.RO] (2025)."},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2025.3577037"},{"key":"e_1_3_3_25_2","unstructured":"D. Chen B. Zhou V. Koltun P. Kr\u00e4henb\u00fchl \u201cLearning by cheating\u201d in Proceedings of the Conference on Robot Learning L. P. Kaelbling D. Kragic K. Sugiura Eds. vol. 100 of Proceedings of Machine Learning Research (PMLR 2020) pp. 66\u201375."},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abc5986"},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","unstructured":"A. Kumar Z. Fu D. Pathak J. Malik \u201cRMA: Rapid motor adaptation for legged robots\u201d in Proceedings of Robotics: Science and Systems (RSS Foundation 2021).","DOI":"10.15607\/RSS.2021.XVII.011"},{"key":"e_1_3_3_28_2","unstructured":"M. Paluch F. Bolli P. Moure X. Deng T. Delbruck \u201cA-NC: Adaptive neural control with implicit online inference of privileged parameters\u201d in Proceedings of the 7th Annual Learning for Dynamics & Control Conference N. Ozay L. Balzano D. Panagou A. Abate Eds. vol. 283 of Proceedings of Machine Learning Research (PMLR 2025) pp. 987\u2013998."},{"key":"e_1_3_3_29_2","unstructured":"T. Yu D. Quillen Z. He R. Julian A. Narayan H. Shively A. Bellathur K. Hausman C. Finn S. Levine \u201cMeta-World: A benchmark and evaluation for multi-task and meta reinforcement learning\u201d in Proceedings of the Conference on Robot Learning L. P. Kaelbling D. Kragic K. Sugiura Eds. vol. 100 of Proceedings of Machine Learning Research (PMLR 2020) pp. 1094\u20131100."},{"key":"e_1_3_3_30_2","unstructured":"T. Yu S. Kumar A. Gupta S. Levine K. Hausman C. Finn \u201cGradient surgery for multi-task learning\u201d in 34th Conference on Neural Information Processing Systems H. Larochelle M. Ranzato R. Hadsell M. Balcan H. Lin Eds. (Curran Associates Inc. 2020) pp. 5824\u20135836."},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","unstructured":"P. Henderson R. Islam P. Bachman J. Pineau D. Precup D. Meger \u201cDeep reinforcement learning that matters\u201d in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence the Thirtieth Innovative Applications of Artificial Intelligence Conference and the Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI Press 2018); doi.org\/10.1609\/aaai.v32i1.11694.","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"e_1_3_3_32_2","unstructured":"Y. Wu E. Mansimov R. B. Grosse S. Liao J. Ba \u201cScalable trust-region method for deep reinforcement learning using Kronecker-factored approximation\u201d in NIPS \u201817: Proceedings of the 31st Annual Conference on Neural Information Processing Systems I. Guyon U. Von Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan R. Garnett Eds. (Curran Associates Inc. 2017) pp. 5285\u20135294."},{"key":"e_1_3_3_33_2","doi-asserted-by":"crossref","unstructured":"H. P. Van Hasselt A. Guez M. Hessel V. Mnih D. Silver \u201cLearning values across many orders of magnitude\u201d in Proceedings of the 30th International Conference on Neural Information Processing Systems D. D. Lee U. von Luxburg R. Garnett M. Sugiyama I. Guyon Eds. (Curran Associates Inc. 2016) pp. 4294\u20134302.","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_3_3_34_2","unstructured":"W. C. Lewis II M. Moll L. E. Kavraki How much do unstated problem constraints limit deep robotic reinforcement learning? arXiv:1909.09282 [cs.RO] (2019)."},{"key":"e_1_3_3_35_2","unstructured":"K. Clary E. Tosch J. Foley D. Jensen Let\u2019s play again: Variability of deep reinforcement learning agents in Atari environments. arXiv:1904.06312 [cs.LG] (2019)."},{"key":"e_1_3_3_36_2","unstructured":"R. Agarwal M. Schwarzer P. S. Castro A. C. Courville M. Bellemare \u201cDeep reinforcement learning at the edge of the statistical precipice\u201d in 35th Annual Conference on Neural Information Processing Systems M. Ranzato A. Beygelzimer Y. Dauphin P. S. Liang J. Wortman Vaughan Eds. (Curran Associates Inc. 2021) pp. 29304\u201329320."},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1903070116"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","unstructured":"A. Molchanov T. Chen W. H\u00f6nig J. A. Preiss N. Ayanian G. S. Sukhatme \u201cSim-to-(Multi)-Real: Transfer of low-level robust control policies to multiple quadrotors\u201d in IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2019) pp. 59\u201366.","DOI":"10.1109\/IROS40897.2019.8967695"},{"key":"e_1_3_3_39_2","volume-title":"Zenodo","author":"Eschmann J.","year":"2025","unstructured":"J. Eschmann, D. Albani, G. Loianno, RAPTOR: A foundation policy for quality control, version 1, Zenodo (2025); https:\/\/doi.org\/10.5281\/zenodo.17096679."},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-021-01383-5"},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","unstructured":"J. Eschmann D. Albani G. Loianno \u201cData-driven system identification of quadrotors subject to motor delays\u201d in IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2024) pp. 8095\u20138102.","DOI":"10.1109\/IROS58592.2024.10801441"},{"key":"e_1_3_3_42_2","unstructured":"G. Alain Y. Bengio Understanding intermediate layers using linear classifier probes. arXiv:1610.01644 [stat.ML] (2016)."},{"key":"e_1_3_3_43_2","unstructured":"A. Dosovitskiy L. Beyer A. Kolesnikov D. Weissenborn X. Zhai T. Unterthiner M. Dehghani M. Minderer G. Heigold S. Gelly J. Uszkoreit N. Houlsby An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929 [cs.CV] (2020)."},{"key":"e_1_3_3_44_2","unstructured":"M. Oquab T. Darcet T. Moutakanni H. Vo M. Szafraniec V. Khalidov P. Fernandez D. Haziza F. Massa A. El-Nouby M. Assran N. Ballas W. Galuba R. Howes P.-Y. Huang S.-W. Li I. Misra M. Rabbat V. Sharma G. Synnaeve H. Xu H. Jegou J. Mairal P. Labatut A. Joulin P. Bojanowski Dinov2: Learning robust visual features without supervision. arXiv:2304.07193 [cs.CV] (2023)."},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.923738"},{"key":"e_1_3_3_46_2","unstructured":"S. O. H. Madgwick \u201cAn efficient orientation filter for inertial and inertial\/magnetic sensor arrays\u201d (University of Bristol and x-io 2010)."},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","unstructured":"P. Kunapuli J. Welde D. Jayaraman V. Kumar \u201cLeveling the playing field: Carefully com-paring classical and learned controllers for quadrotor trajectory tracking\u201d in Proceedings of Robotics: Science and Systems (RSS Foundation 2025) 10.15607\/RSS.2025.XXI.116.","DOI":"10.15607\/RSS.2025.XXI.116"},{"key":"e_1_3_3_48_2","unstructured":"S. Ross B. Chaib-draa J. Pineau \u201cBayes-adaptive POMDPs\u201d in 21st Annual Conference on Neural Information Processing Systems J. Platt D. Koller Y. Singer S. Roweis Eds. (Curran Associates Inc. 2007) pp. 1225\u20131232."},{"key":"e_1_3_3_49_2","unstructured":"D. Koller N. Friedman Probabilistic Graphical Models: Principles and Techniques (MIT Press 2009)."},{"key":"e_1_3_3_50_2","doi-asserted-by":"crossref","unstructured":"J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Publishers Inc. 1988).","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"e_1_3_3_51_2","unstructured":"OpenAI Foundation \u201cSolving Rubik\u2019s cube with a robot hand \u201d 15 October 2019; https:\/\/openai.com\/index\/solving-rubiks-cube\/."},{"key":"e_1_3_3_52_2","unstructured":"J. X. Wang Z. Kurth-Nelson D. Tirumala H. Soyer J. Z. Leibo R. Munos C. Blundell D. Kumaran M. Botvinick Learning to reinforcement learn. arXiv:1611.05763 [cs.LG] (2016)."},{"key":"e_1_3_3_53_2","unstructured":"Y. Duan J. Schulman X. Chen P. L. Bartlett I. Sutskever P. Abbeel RL2: Fast reinforcement learning via slow reinforcement learning. arXiv:1611.02779 [cs.AI] (2016)."},{"key":"e_1_3_3_54_2","unstructured":"Open AI J. Achiam S. Adler S. Agarwal L. Ahmad I. Akkaya F. L. Aleman D. Almeida J. Altenschmidt S. Altman S. Anadkat R. Avila I. Babuschkin S. Balaji V. Balcom P. Baltescu H. Bao M. Bavarian J. Belgum I. Bello J. Berdine G. Bernadett-Shapiro C. Berner L. Bogdonoff O. Boiko M. Boyd A.-L. Brakman G. Brockman T. Brooks M. Brundage K. Button T. Cai R. Campbell A. Cann B. Carey C. Carlson R. Carmichael B. Chan C. Chang F. Chantzis D. Chen S. Chen R. Chen J. Chen M. Chen B. Chess C. Cho C. Chu H. W. Chung D. Cummings J. Currier Y. Dai C. Decareaux T. Degry N. Deutsch D. Deville A. Dhar D. Dohan S. Dowling S. Dunning A. Ecoffet A. Eleti T. Eloundou D. Farhi L. Fedus N. Felix S. P. Fishman J. Forte I. Fulford L. Gao E. Georges C. Gibson V. Goel T. Gogineni G. Goh R. Gontijo-Lopes J. Gordon M. Grafstein S. Gray R. Greene J. Gross S. S. Gu Y. Guo C. Hallacy J. Han J. Harris Y. He M. Heaton J. Heidecke C. Hesse A. Hickey W. Hickey P. Hoeschele B. Houghton K. Hsu S. Hu X. Hu J. Huizinga S. Jain S. Jain J. Jang A. Jiang R. Jiang H. Jin D. Jin S. Jomoto B. Jonn H. Jun T. Kaftan \u0141. Kaiser A. Kamali I. Kanitscheider N. S. Keskar T. Khan L. Kilpatrick J. W. Kim C. Kim Y. Kim J. H. Kirchner J. Kiros M. Knight D. Kokotajlo \u0141. Kondraciuk A. Kondrich A. Konstantinidis K. Kosic G. Krueger V. Kuo M. Lampe I. Lan T. Lee J. Leike J. Leung D. Levy C. M. Li R. Lim M. Lin S. Lin M. Litwin T. Lopez R. Lowe P. Lue A. Makanju K. Malfacini S. Manning T. Markov Y. Markovski B. Martin K. Mayer A. Mayne B. M. Grew Scott Mayer Mc Kinney C. M. Leavey P. M. Millan J. M. Neil D. Medina A. Mehta J. Menick L. Metz A. Mishchenko P. Mishkin V. Monaco E. Morikawa D. Mossing T. Mu M. Murati O. Murk D. M\u00e9ly A. Nair R. Nakano R. Nayak A. Neelakantan R. Ngo H. Noh L. Ouyang C. O\u2019Keefe J. Pachocki A. Paino J. Palermo A. Pantuliano G. Parascandolo J. Parish E. Parparita A. Passos M. Pavlov A. Peng A. Perelman Filipe de Avila Belbute Peres M. Petrov Henrique Ponde de Oliveira Pinto Michael (Rai)Pokorny M. Pokrass V. H. Pong T. Powell A. Power B. Power E. Proehl R. Puri A. Radford J. Rae A. Ramesh C. Raymond F. Real K. Rimbach C. Ross B. Rotsted H. Roussez N. Ryder M. Saltarelli T. Sanders S. Santurkar G. Sastry H. Schmidt D. Schnurr J. Schulman D. Selsam K. Sheppard T. Sherbakov J. Shieh S. Shoker P. Shyam S. Sidor E. Sigler M. Simens J. Sitkin K. Slama I. Sohl B. Sokolowsky Y. Song N. Staudacher F. P. Such N. Summers I. Sutskever J. Tang N. Tezak M. B. Thompson P. Tillet A. Tootoonchian E. Tseng P. Tuggle N. Turley J. Tworek Juan Felipe Cer\u00f3n Uribe A. Vallone A. Vijayvergiya C. Voss C. Wainwright J. J. Wang A. Wang B. Wang J. Ward J. Wei C. J. Weinmann A. Welihinda P. Welinder J. Weng L. Weng M. Wiethoff D. Willner C. Winter S. Wolrich H. Wong L. Workman S. Wu J. Wu M. Wu K. Xiao T. Xu S. Yoo K. Yu Q. Yuan W. Zaremba R. Zellers C. Zhang M. Zhang S. Zhao T. Zheng J. Zhuang W. Zhuk B. Zoph Gpt-4 technical report. arXiv:2303.08774 [cs.CL] (2023)."},{"key":"e_1_3_3_55_2","doi-asserted-by":"crossref","unstructured":"K. Cho B. van Merrienboer C. Gulcehre D. Bahdanau F. Bougares H. Schwenk Y. Bengio Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078 [cs.CL] (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_3_56_2","unstructured":"S. Ross G. Gordon D. Bagnell \u201cA reduction of imitation learning and structured prediction to no-regret online learning\u201d in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics G. Gordon D. Dunson M. Dud\u00edk Eds. vol. 15 (PMLR 2011) pp. 627\u2013635."},{"key":"e_1_3_3_57_2","unstructured":"C. Moler \u201cMatrix computation on distributed memory multiprocessors\u201d in Hypercube Multiprocessors Proceedings of the First Conference on Hypercube Multiprocessors M. T. Heath Ed. (SIAM 1986) pp. 181\u2013195."},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2246292"},{"key":"e_1_3_3_59_2","doi-asserted-by":"crossref","unstructured":"S. S\u00e4rkk\u00e4 A. Solin Applied Stochastic Differential Equations vol. 10 (Cambridge Univ. Press 2019).","DOI":"10.1017\/9781108186735"},{"key":"e_1_3_3_60_2","unstructured":"Open AI Christopher Berner G. Brockman B. Chan V. Cheung P. D\u0119biak C. Dennison D. Farhi Q. Fischer S. Hashme C. Hesse R. J\u00f3zefowicz S. Gray C. Olsson J. Pachocki M. Petrov Henrique P. d.O. Pinto J. Raiman T. Salimans J. Schlatter J. Schneider S. Sidor I. Sutskever J. Tang F. Wolski S. Zhang Dota 2 with large scale deep reinforcement learning. arXiv:1912.06680 [cs.LG] (2019)."}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.aec1481","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.aec1481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:00:58Z","timestamp":1779300058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.aec1481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":59,"journal-issue":{"issue":"114","published-print":{"date-parts":[[2026,5,13]]}},"alternative-id":["10.1126\/scirobotics.aec1481"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.aec1481","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"2025-09-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eaec1481"}}