{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:17:34Z","timestamp":1770225454377,"version":"3.49.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Canada CIFAR AI Chairs Program"},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1109\/lra.2020.3015449","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T21:33:59Z","timestamp":1597181639000},"page":"6654-6661","source":"Crossref","is-referenced-by-count":7,"title":["Heteroscedastic Uncertainty for Robust Generative Latent Dynamics"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6886-2633","authenticated-orcid":false,"given":"Oliver","family":"Limoyo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6278-9575","authenticated-orcid":false,"given":"Bryan","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4171-8006","authenticated-orcid":false,"given":"Filip","family":"Maric","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7808-0045","authenticated-orcid":false,"given":"Brandon","family":"Wagstaff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. Rupam","family":"Mahmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5528-6136","authenticated-orcid":false,"given":"Jonathan","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"561","article-title":"Benchmarking reinforcement learning algorithms on real-world robots","volume":"87","author":"mahmood","year":"0","journal-title":"Proc Conf Robot Learn"},{"key":"ref38","article-title":"Playing Atari with deep reinforcement learning","author":"mnih","year":"0","journal-title":"Proc NIPS’2007 Deep Learn Workshop"},{"key":"ref33","author":"murphy","year":"2012","journal-title":"Machine Learning A Probabilistic Perspective"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2004.834121"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref37","article-title":"OpenAI gym","author":"brockman","year":"0"},{"key":"ref36","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","author":"diamond","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.15"},{"key":"ref34","article-title":"Generalized product of experts for automatic and principled fusion of Gaussian process predictions","author":"cao","year":"0","journal-title":"Proc Mod Nonparametrics 3 Autom Learn Pipeline Workshop NIPS"},{"key":"ref10","article-title":"From pixels to torques: Policy learning with deep dynamical models","author":"wahlstr\u00f6m","year":"0","journal-title":"Proc ICML Deep Learn Workshop"},{"key":"ref11","first-page":"2746","article-title":"Embed to control: A locally linear latent dynamics model for control from raw images","author":"watter","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref12","first-page":"1751","article-title":"Robust locally-linear controllable embedding","volume":"84","author":"banijamali","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref13","first-page":"1462","article-title":"Draw: A recurrent neural network for image generation","author":"gregor","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","first-page":"2980","article-title":"A recurrent latent variable model for sequential data","volume":"28","author":"chung","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref15","first-page":"4376","article-title":"Backprop KF: Learning discriminative deterministic state estimators","author":"haarnoja","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref16","first-page":"2101","article-title":"Structured inference networks for nonlinear state space models","author":"krishnan","year":"0","journal-title":"Proc Assoc Advancement Artif Intell Conf"},{"key":"ref17","first-page":"7444","article-title":"Solar: Deep structured representations for model-based reinforcement learning","author":"zhang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","first-page":"2555","article-title":"Learning latent dynamics for planning from pixels","author":"hafner","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","first-page":"2450","article-title":"Recurrent world models facilitate policy evolution","author":"ha","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"ref4","first-page":"2946","article-title":"Composing graphical models with neural networks for structured representations and fast inference","volume":"29","author":"johnson","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594386"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s40300-019-00155-4"},{"key":"ref6","first-page":"465","article-title":"PILCO: A model-based and data-efficient approach to policy search","author":"deisenroth","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2514\/3.3166"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015619"},{"key":"ref8","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1768-8_11"},{"key":"ref2","first-page":"3601","article-title":"A disentangled recognition and nonlinear dynamics model for unsupervised learning","volume":"30","author":"fraccaro","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref9","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref1","article-title":"Deep variational Bayes filters: Unsupervised learning of state space models from raw data","author":"karl","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","first-page":"528","article-title":"Dyna-style planning with linear function approximation and prioritized sweeping","author":"sutton","year":"0","journal-title":"Proc Conf Uncertainty Artif Intell"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1088\/0954-898X\/6\/3\/011"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1049\/ip-vis:19941330"},{"key":"ref24","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref23","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"gal","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3192-0_9"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2017.XIII.064"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9133350\/09164983.pdf?arnumber=9164983","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:33:44Z","timestamp":1651080824000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9164983\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":39,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/lra.2020.3015449","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10]]}}}