{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T03:23:09Z","timestamp":1773976989291,"version":"3.50.1"},"reference-count":42,"publisher":"SAGE Publications","issue":"2-3","license":[{"start":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T00:00:00Z","timestamp":1529366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p> The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT\u2019s performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution. <\/jats:p>","DOI":"10.1177\/0278364918780322","type":"journal-article","created":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T06:23:29Z","timestamp":1529475809000},"page":"162-181","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":27,"title":["Importance sampling for online planning under uncertainty"],"prefix":"10.1177","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9804-6204","authenticated-orcid":false,"given":"Yuanfu","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore"}]},{"given":"Haoyu","family":"Bai","sequence":"additional","affiliation":[{"name":"Movel AI Pte Ltd, Singapore"}]},{"given":"David","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore"}]},{"given":"Wee Sun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore"}]}],"member":"179","published-online":{"date-parts":[[2018,6,19]]},"reference":[{"key":"bibr1-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139219"},{"key":"bibr2-0278364918780322","first-page":"1","volume-title":"Robotics: science and systems VII","author":"Bai H","year":"2012"},{"key":"bibr3-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17452-0_11"},{"key":"bibr4-0278364918780322","volume-title":"Dynamic Programming and Optimal Control","author":"Bertsekas D","year":"2005"},{"key":"bibr5-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1999.772447"},{"key":"bibr6-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1145\/2438653.2438664"},{"key":"bibr7-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273531"},{"key":"bibr8-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21617-1"},{"key":"bibr9-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1007\/BF01891840"},{"key":"bibr10-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17452-0_12"},{"key":"bibr11-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3171"},{"key":"bibr12-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2007.364201"},{"key":"bibr13-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(98)00023-X"},{"key":"bibr14-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1002\/9783527617395"},{"key":"bibr15-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1023\/A:1017932429737"},{"key":"bibr16-0278364918780322","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"Koller D","year":"2009"},{"key":"bibr17-0278364918780322","volume-title":"Proceedings of the International Workshop on the Algorithmic Foundations of Robotics XII (WAFR)","author":"Koval M","year":"2016"},{"key":"bibr18-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915594474"},{"key":"bibr19-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2004.1389627"},{"key":"bibr20-0278364918780322","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2008.IV.009"},{"key":"bibr21-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1177\/027836498400300101"},{"key":"bibr22-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1007\/BF01840369"},{"key":"bibr23-0278364918780322","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2015.2452199"},{"key":"bibr24-0278364918780322","unstructured":"Owen AB (2013) Monte Carlo Theory, Methods and Examples. 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