{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:39:25Z","timestamp":1782923965387,"version":"3.54.5"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1109\/iros40897.2019.8968089","type":"proceedings-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T23:53:51Z","timestamp":1580428431000},"page":"3965-3972","source":"Crossref","is-referenced-by-count":66,"title":["Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation"],"prefix":"10.1109","author":[{"given":"Mayur J.","family":"Bency","sequence":"first","affiliation":[{"name":"University of California,Department of Electrical and Computer Engineering,San Diego, La Jolla,CA,USA,92093"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed H.","family":"Qureshi","sequence":"additional","affiliation":[{"name":"University of California,Department of Electrical and Computer Engineering,San Diego, La Jolla,CA,USA,92093"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Yip","sequence":"additional","affiliation":[{"name":"University of California,Department of Electrical and Computer Engineering,San Diego, La Jolla,CA,USA,92093"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2498841"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)E0045-M"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/HUMANOIDS.2016.7803340"},{"key":"ref13","first-page":"1","article-title":"Guided policy search","author":"levine","year":"2013","journal-title":"International Conference on Machine Learning"},{"key":"ref14","article-title":"Mastering chess and shogi by self-play with a general reinforcement learning algorithm","author":"silver","year":"2017","journal-title":"arXiv preprint arXiv 1712 01815"},{"key":"ref15","first-page":"2154","article-title":"Value iteration networks","author":"tamar","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1177\/0278364910371999"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2010.936947"},{"key":"ref18","first-page":"496","article-title":"Fastron: An online learning-based model and active learning strategy for proxy collision detection","author":"das","year":"2017","journal-title":"Conference on Robot Learning"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2015.02.007"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-015-9518-0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1177\/0278364904045481"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSSC.1968.300136"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-8997-2_29"},{"key":"ref7","author":"lavalle","year":"1998","journal-title":"Rapidly-Exploring Random Trees A New Tool for Path Planning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2008.4543636"},{"key":"ref1","article-title":"Motion planning networks","author":"qureshi","year":"2018","journal-title":"arXiv preprint arXiv 1806 05767"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2009.5152817"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80125-X"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2000.844730"},{"key":"ref21","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2015.XI.045"},{"key":"ref23","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"}],"event":{"name":"2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","location":"Macau, China","start":{"date-parts":[[2019,11,3]]},"end":{"date-parts":[[2019,11,8]]}},"container-title":["2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8957008\/8967518\/08968089.pdf?arnumber=8968089","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T18:08:18Z","timestamp":1757095698000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8968089\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/iros40897.2019.8968089","relation":{},"subject":[],"published":{"date-parts":[[2019,11]]}}}