{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T15:55:05Z","timestamp":1784390105179,"version":"3.55.0"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1935329"],"award-info":[{"award-number":["1935329"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Robot."],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1109\/tro.2020.3006716","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T21:46:00Z","timestamp":1596491160000},"page":"48-66","source":"Crossref","is-referenced-by-count":257,"title":["Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2104-2333","authenticated-orcid":false,"given":"Ahmed Hussain","family":"Qureshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5248-7565","authenticated-orcid":false,"given":"Yinglong","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2573-2510","authenticated-orcid":false,"given":"Anthony","family":"Simeonov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9689-0172","authenticated-orcid":false,"given":"Michael C.","family":"Yip","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/978-3-319-29363-9_17","article-title":"Demonstration-guided motion planning","author":"ye","year":"2017","journal-title":"Robotics Research"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139284"},{"key":"ref33","article-title":"Data-efficient deep reinforcement learning for dexterous manipulation","author":"popov","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989384"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202141"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2012.6224742"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022672621406"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2008.4543787"},{"key":"ref34","article-title":"Composing task-agnostic policies with deep reinforcement learning","author":"qureshi","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2012.2205651"},{"key":"ref62","article-title":"Adversarial imitation via variational inverse reinforcement learning","author":"qureshi","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref61","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.00038"},{"key":"ref28","first-page":"1334","article-title":"End-to-end training of deep visuomotor policies","volume":"17","author":"levine","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref64","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref27","first-page":"1329","article-title":"Benchmarking deep reinforcement learning for continuous control","author":"duan","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref65","first-page":"1","article-title":"Point cloud library (PCL)","author":"rusu","year":"2011","journal-title":"Proc IEEE Int Conf Robot Autom"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917710318"},{"key":"ref2","first-page":"2997","article-title":"Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic","author":"gammell","year":"2014","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robots Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2018.06.013"},{"key":"ref22","volume":"135","author":"sutton","year":"1998","journal-title":"Introduction to Reinforcement Learning"},{"key":"ref21","article-title":"Deep conditional generative models for heuristic search on graphs with expensive-to-evaluate edges","author":"hou","year":"0"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2300000021","article-title":"A survey on policy search for robotics","volume":"2","author":"deisenroth","year":"2013","journal-title":"Foundations and Trends in Robotics"},{"key":"ref23","article-title":"Continuous control with deep reinforcement learning","author":"lillicrap","year":"2015"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"ref50","first-page":"833","article-title":"Contractive auto-encoders: Explicit invariance during feature extraction","author":"rifai","year":"2011","journal-title":"Proc 28th Int Conf Int Conf Mach Learn"},{"key":"ref51","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref59","author":"skiena","year":"1991","journal-title":"Implementing Discrete Mathematics Combinatorics and Graph Theory with Mathematica"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509683"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2000.844730"},{"key":"ref56","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting.","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref55","first-page":"350","article-title":"Experience replay for continual learning","author":"rolnick","year":"2019","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref54","first-page":"3302","article-title":"Selective experience replay for lifelong learning","author":"isele","year":"2018","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref53","first-page":"6467","article-title":"Gradient episodic memory for continual learning","author":"lopez-paz and","year":"2017","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.694"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/0196-8858(83)90014-3"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8968089"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/359156.359164"},{"key":"ref12","author":"canny","year":"1988","journal-title":"The Complexity of Robot Motion Planning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1985.6313352"},{"key":"ref14","article-title":"Rapidly-exploring random trees: A new tool for path planning","author":"lavalle","year":"1998"},{"key":"ref15","article-title":"Probabilistic roadmaps for robot path planning","author":"kavraki","year":"1998"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139603"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-015-9518-0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO.2013.6739744"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2015.02.007"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546877"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139620"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-8997-2_29"},{"key":"ref5","volume":"124","author":"latombe","year":"2012","journal-title":"Robot Motion Planning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793889"},{"key":"ref7","first-page":"6161","article-title":"The provable virtue of laziness in motion planning","author":"haghtalab","year":"2018","journal-title":"Proc 28th Int Conf Automated Planning Scheduling"},{"key":"ref49","article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","volume":"3","author":"higgins","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593772"},{"key":"ref46","first-page":"4732","article-title":"Universal planning networks: Learning generalizable representations for visuomotor control","author":"srinivas","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref45","first-page":"2154","article-title":"Value iteration networks","author":"tamar","year":"2016","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref48","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2901898"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28872-7_38"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460730"},{"key":"ref44","first-page":"271","article-title":"Learning heuristic search via imitation","author":"bhardwaj","year":"0","journal-title":"Proc Conf Robot Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594028"}],"container-title":["IEEE Transactions on Robotics"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/8860\/9347829\/9154607-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8860\/9347829\/09154607.pdf?arnumber=9154607","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:51:35Z","timestamp":1649443895000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9154607\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":65,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tro.2020.3006716","relation":{},"ISSN":["1552-3098","1941-0468"],"issn-type":[{"value":"1552-3098","type":"print"},{"value":"1941-0468","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2]]}}}