{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T20:38:18Z","timestamp":1776458298189,"version":"3.51.2"},"reference-count":84,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000104","name":"NASA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Leadership Initiative","award":["80NSSC20M0163"],"award-info":[{"award-number":["80NSSC20M0163"]}]},{"name":"Early Stage Innovations program"},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Robot."],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1109\/tro.2022.3154715","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T20:15:17Z","timestamp":1651608917000},"page":"2888-2907","source":"Crossref","is-referenced-by-count":37,"title":["Safe Active Dynamics Learning and Control: A Sequential Exploration\u2013Exploitation Framework"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8209-3604","authenticated-orcid":false,"given":"Thomas","family":"Lew","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0904-8847","authenticated-orcid":false,"given":"Apoorva","family":"Sharma","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Harrison","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Bylard","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-4337","authenticated-orcid":false,"given":"Marco","family":"Pavone","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-053018-023825"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.218"},{"key":"ref3","first-page":"1","article-title":"End-to-end training of deep visuomotor policies","volume":"17","author":"Levine","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2019.8795639"},{"key":"ref5","first-page":"1","article-title":"Distributionally robust chance constrained data-enabled predictive control","volume-title":"Proc. IEEE Conf. Decis. Control","author":"Coulson","year":"2019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CDC42340.2020.9303965"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3000182"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3166872"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1177\/027836498700600303"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2020.3000190"},{"key":"ref11","first-page":"1","article-title":"Active learning for nonlinear system identification with guarantees","volume":"23","author":"Mania","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref12","first-page":"1","article-title":"Information theoretic regret bounds for online nonlinear control","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Kakade","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794351"},{"key":"ref14","article-title":"Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta...-hook,","author":"Schmidhuber","year":"1987"},{"key":"ref15","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"48","author":"Santoro","year":"2016"},{"key":"ref16","first-page":"205","article-title":"Meta-learning by adjusting priors based on extended PAC-Bayes theory","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Amit","year":"2018"},{"key":"ref17","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2021.xvii.056"},{"key":"ref19","first-page":"1","article-title":"Learning to adapt in dynamic real-world environments through meta-reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Nagabandi","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3057046"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-44051-0_19"},{"key":"ref22","volume-title":"Gaussian Processes for Machine Learning","author":"Williams","year":"2006"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16912"},{"key":"ref24","first-page":"908","article-title":"Safe model-based reinforcement learning with stability guarantees","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Berkenkamp","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619572"},{"key":"ref26","first-page":"19","article-title":"Improved algorithms for linear stochastic bandits","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Abbasi-Yadkori","year":"2011"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2010.2044948"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2019.2949757"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2975759"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.23919\/ECC51009.2020.9143595"},{"key":"ref32","first-page":"1","article-title":"Safety guarantees for planning based on iterative Gaussian processes","volume-title":"Proc. IEEE Conf. Decis. Control","author":"Polymenakos","year":"2020"},{"key":"ref33","first-page":"781","article-title":"Probabilistic safety constraints for learned high relative degree system dynamics","volume-title":"Proc. 2nd Annu. Conf. Learn. Dyn. Control","author":"Khojasteh","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CDC42340.2020.9304395"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2020.XVI.019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2017.XIII.068"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2011.2161160"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aau5872"},{"key":"ref39","article-title":"Safe exploration in reinforcement learning: Theory and applications in robotics","author":"Berkenkamp","year":"2018"},{"key":"ref40","first-page":"1","article-title":"Lyapunov-based safe policy optimization for continuous control","volume-title":"Proc. Conf. Robot Learn.","author":"Chow","year":"2020"},{"key":"ref41","first-page":"1","article-title":"Safe learning and control using meta-learning","volume-title":"Proc. Robot.: Sci. Syst., Workshop Robust Autonomy","author":"Lew","year":"2019"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3044033"},{"key":"ref43","article-title":"On the problem of reformulating systems with uncertain dynamics as a stochastic differential equation","author":"Lew","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-019-01040-y"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560869"},{"issue":"127","key":"ref46","first-page":"1","article-title":"Dual control for approximate Bayesian reinforcement learning","volume":"17","author":"Klenske","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.2280"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1666"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2014.7039601"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7798979"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619312"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2018.07.004"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CDC45484.2021.9682880"},{"key":"ref54","first-page":"1","article-title":"Control adaptation via meta-learning dynamics","volume-title":"Proc. NeurIPS Workshop Meta-Learn.","author":"Harrison","year":"2018"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44668-0_13"},{"key":"ref56","article-title":"Uncertainty and efficiency in adaptive robot learning and control","author":"Harrison","year":"2021"},{"key":"ref57","first-page":"2171","article-title":"Scalable Bayesian optimization using deep neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Snoek","year":"2015"},{"key":"ref58","first-page":"1","article-title":"Accurate uncertainties for deep learning using calibrated regression","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kuleshov","year":"2018"},{"key":"ref59","first-page":"1015","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Srinivas","year":"2010"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2948352"},{"key":"ref61","first-page":"1","article-title":"Adaptive meta-learning for identification of RoverTerrain dynamics","volume-title":"Proc. Int. Symp. Artif. Intell., Robot. Automat. Space","author":"Banerjee","year":"2020"},{"key":"ref62","volume-title":"Econometric Analysis","author":"Greene","year":"2002"},{"key":"ref63","first-page":"1","article-title":"Sampling-based reachability analysis: A random set theory approach with adversarial sampling","volume-title":"Proc. Conf. Robot Learn.","author":"Lew","year":"2020"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2874704"},{"key":"ref65","volume-title":"Model Predictive Control: Theory and Design","author":"Rawlings","year":"2013"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01094-1_1"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100635"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.4.590"},{"key":"ref69","first-page":"844","article-title":"On kernelized multi-armed bandits","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"70","author":"Chowdhury","year":"2017"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2012.6315201"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2017.8263977"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2020.XVI.087"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/3302504.3311806"},{"key":"ref74","article-title":"A simple and efficient sampling-based algorithm for general reachability analysis","volume-title":"Proc. 4th Annu. Conf. Learn. Dyn. Control","author":"Lew","year":"2022"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7798816"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794205"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/s12532-020-00179-2"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1201\/9781315136370"},{"key":"ref79","first-page":"1","article-title":"Astrobee robot software: Enabling mobile autonomy on the ISS","volume-title":"Proc. Int. Symp. Artif. Intell., Robot. Automat. Space","author":"Fluckiger","year":"2018"},{"key":"ref80","first-page":"1","article-title":"Random features for large-scale kernel machines","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Rahimi","year":"2007"},{"key":"ref81","first-page":"10292","article-title":"Efficiently sampling functions from Gaussian process posteriors","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wilson","year":"2020"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989202"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2020.109009"},{"key":"ref84","first-page":"1","article-title":"Continuous meta-learning without tasks","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Harrison","year":"2020"}],"container-title":["IEEE Transactions on Robotics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8860\/9910236\/09767193.pdf?arnumber=9767193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:45:39Z","timestamp":1705963539000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9767193\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":84,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tro.2022.3154715","relation":{},"ISSN":["1552-3098","1941-0468"],"issn-type":[{"value":"1552-3098","type":"print"},{"value":"1941-0468","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10]]}}}