{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:38:56Z","timestamp":1782409136031,"version":"3.54.5"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"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":[[2021,5,30]]},"DOI":"10.1109\/icra48506.2021.9560951","type":"proceedings-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T20:28:35Z","timestamp":1634675315000},"page":"7829-7835","source":"Crossref","is-referenced-by-count":47,"title":["NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments"],"prefix":"10.1109","author":[{"given":"Daniel","family":"Dugas","sequence":"first","affiliation":[{"name":"ETH Zurich,Autonomous Systems Lab,Zurich,Switzerland,8092"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Nieto","sequence":"additional","affiliation":[{"name":"ETH Zurich,Autonomous Systems Lab,Zurich,Switzerland,8092"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland","family":"Siegwart","sequence":"additional","affiliation":[{"name":"ETH Zurich,Autonomous Systems Lab,Zurich,Switzerland,8092"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jen Jen","family":"Chung","sequence":"additional","affiliation":[{"name":"ETH Zurich,Autonomous Systems Lab,Zurich,Switzerland,8092"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593871"},{"key":"ref11","first-page":"2450","article-title":"Recurrent world models facilitate policy evolution","author":"ha","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref12","article-title":"Learning real-world robot policies by dreaming","author":"piergiovanni","year":"2018"},{"key":"ref13","first-page":"1231","article-title":"Learning to simulate dynamic environments with gamegan","author":"kim","year":"2020","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"ref14","article-title":"Improving language understanding by generative pre-training","author":"radford","year":"0"},{"key":"ref15","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref16","article-title":"Language models are few-shot learners","author":"brown","year":"2020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2008.4543489"},{"key":"ref18","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref19","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920916531"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2869644"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794134"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989037"},{"key":"ref8","article-title":"Ian: Multi-behavior navigation planning for robots in real, crowded environments","author":"dugas","year":"0","journal-title":"2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"},{"key":"ref7","article-title":"Robot navigation in crowded environments using deep reinforcement learning","author":"liu","year":"0","journal-title":"2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989182"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202312"},{"key":"ref1","article-title":"Agent57: Outperforming the atari human benchmark","author":"badia","year":"2020"},{"key":"ref20","article-title":"Stable baselines","author":"hill","year":"2018","journal-title":"Github Repository"}],"event":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","location":"Xi'an, China","start":{"date-parts":[[2021,5,30]]},"end":{"date-parts":[[2021,6,5]]}},"container-title":["2021 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9560720\/9560666\/09560951.pdf?arnumber=9560951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T19:22:41Z","timestamp":1659468161000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9560951\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/icra48506.2021.9560951","relation":{},"subject":[],"published":{"date-parts":[[2021,5,30]]}}}