{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:56:17Z","timestamp":1784645777866,"version":"3.55.0"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2001206"],"award-info":[{"award-number":["U2001206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Talent Program","award":["2019JC05X328"],"award-info":[{"award-number":["2019JC05X328"]}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2020A0505100064"],"award-info":[{"award-number":["2020A0505100064"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DEGP Key Project","award":["2018KZDXM058"],"award-info":[{"award-number":["2018KZDXM058"]}]},{"name":"Shenzhen Science and Technology Program","award":["RCJC20200714114435012"],"award-info":[{"award-number":["RCJC20200714114435012"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20210324120213036"],"award-info":[{"award-number":["JCYJ20210324120213036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1109\/lra.2021.3116700","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T20:58:49Z","timestamp":1633035529000},"page":"65-72","source":"Crossref","is-referenced-by-count":40,"title":["Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3187-1453","authenticated-orcid":false,"given":"Tan","family":"Zhang","sequence":"first","affiliation":[{"name":"Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kefang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiatao","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2742-6947","authenticated-orcid":false,"given":"Wing-Yue Geoffrey","family":"Louie","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3212-0544","authenticated-orcid":false,"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29363-9_18"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2017.XIII.034"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"ref6","first-page":"1","article-title":"Towards monocular vision based obstacle avoidance through deep reinforcement learning","volume-title":"Proc. RSS Workshop New Front. for Deep Learn. Robot.","author":"Xie"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2019.XV.026"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2011.07.004"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917710318"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-59496-5_337"},{"key":"ref12","first-page":"334","article-title":"Transferring end-to-end visuo- motor control from simulation to real world for a multi-stage task","volume-title":"Proc. Conf. Rob. Learn.","author":"James"},{"key":"ref13","first-page":"4243","article-title":"Using simulation and domain adaptation to improve efficiency of deep robotic grasping","volume-title":"Proc. IEEE Int. Conf. Robot. Automat.","author":"Tobin"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461041"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1993.5.4.613"},{"key":"ref16","first-page":"1861","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume-title":"Proc. Int. Conf. Mach. Learn, PMRL","author":"Haarnoja"},{"key":"ref17","article-title":"Towards adapting deep visuomotor representations from simulated to real environments","volume":"abs\/1511.07111","author":"Tzeng","year":"2015","journal-title":"CoRR"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/1729881417703930"},{"key":"ref19","first-page":"496","article-title":"Fastron: An online learning-based model and active learning strategy for proxy collision detection","volume-title":"Proc. Conf. Rob. Learn.","author":"Das"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793889"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2954952"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2018.8550363"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO.2018.8665248"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206049"},{"key":"ref25","article-title":"Deep successor reinforcement learning","volume":"abs\/1606.02396","author":"Kulkarni","year":"2016","journal-title":"CoRR"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/bf00992696"},{"key":"ref27","first-page":"1008","article-title":"Actor-critic algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Konda"},{"key":"ref28","first-page":"387","article-title":"Deterministic policy gradient algorithms","volume-title":"Proc. Int. Conf. Mach. Learn., PMRL","author":"Silver"},{"key":"ref29","first-page":"1","article-title":"Continuous control with deep reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lillicrap"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6386109"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9568780\/09555228.pdf?arnumber=9555228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T22:51:37Z","timestamp":1705013497000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9555228\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":30,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/lra.2021.3116700","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]}}}