{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:17:43Z","timestamp":1766067463310,"version":"3.37.3"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100007863","name":"Rice University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,27]]},"DOI":"10.1109\/iros51168.2021.9636651","type":"proceedings-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T20:45:38Z","timestamp":1639687538000},"page":"2511-2518","source":"Crossref","is-referenced-by-count":11,"title":["HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization"],"prefix":"10.1109","author":[{"given":"Mark","family":"Moll","sequence":"first","affiliation":[{"name":"Rice University,Department of Computer Science,Houston,TX,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Constantinos","family":"Chamzas","sequence":"additional","affiliation":[{"name":"Rice University,Department of Computer Science,Houston,TX,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zachary","family":"Kingston","sequence":"additional","affiliation":[{"name":"Rice University,Department of Computer Science,Houston,TX,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lydia E.","family":"Kavraki","sequence":"additional","affiliation":[{"name":"Rice University,Department of Computer Science,Houston,TX,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915614386"},{"key":"ref33","first-page":"403","article-title":"A single-query bi-directional probabilistic roadmap planner with lazy collision checking","author":"s\u00e1nchez","year":"2001","journal-title":"Robotics Research The Tenth International Symposium"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2011.2160466"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1142\/S0218195999000285"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2000.844107"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TRO.2016.2539377","article-title":"Asymptotically near-optimal rrt for fast, high-quality motion planning","volume":"32","author":"salzman","year":"2016","journal-title":"IEEE Trans on Robotics"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197338"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/978-3-319-16595-0_27","article-title":"RRTX: Real-time motion planning\/replanning for environments with unpredictable obstacles","author":"otte","year":"2015","journal-title":"Algorithmic Foundations of Robotics XI Springer"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630908"},{"article-title":"Tune: A research platform for distributed model selection and training","year":"2018","author":"liaw","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989504"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594183"},{"article-title":"New techniques for algorithm portfolio design","year":"2012","author":"streeter","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MRA.2012.2205651","article-title":"The Open Motion Planning Library","volume":"19","author":"s\u00b8ucan","year":"2012","journal-title":"IEEE Robotics & Automation Magazine"},{"journal-title":"Kendall’s Advanced Theory of Statistics","year":"1994","author":"stuart","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2012.2234312"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919890396"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","author":"hornung","year":"2013","journal-title":"Autonomous Robots"},{"article-title":"Population based training of neural networks","year":"2017","author":"jaderberg","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1177\/02783640122067453"},{"key":"ref3","first-page":"2113","article-title":"Gradient-based hyper-parameter optimization through reversible learning","author":"maclaurin","year":"2015","journal-title":"International Conference on Machine Learning"},{"article-title":"BOAH: A tool suite for multi-fidelity bayesian optimization & analysis of hyperparameters","year":"2019","author":"lindauer","key":"ref6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/70.508439"},{"article-title":"Stochastic hyperparameter optimization through hypernetworks","year":"2018","author":"lorraine","key":"ref5"},{"key":"ref8","first-page":"2951","article-title":"Practical bayesian optimization of machine learning algorithms","volume":"25","author":"snoek","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref7","first-page":"1437","volume":"80","author":"falkner","year":"2018","journal-title":"Intl conf on Machine Learning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2302442"},{"key":"ref9","first-page":"6765","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"li","year":"2017","journal-title":"Journal of Machine Learning Research"},{"journal-title":"The Complexity of Robot Motion Planning","year":"1988","author":"canny","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-50517-2_79"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2013.2240176"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6386194"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2015.2448276"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-011-9228-z"},{"article-title":"Learning sampling distributions using local 3d workspace decompositions for motion planning in high dimensions","year":"2020","author":"chamzas","key":"ref26"},{"journal-title":"MoveIt!","year":"0","author":"?ucan","key":"ref25"}],"event":{"name":"2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","start":{"date-parts":[[2021,9,27]]},"location":"Prague, Czech Republic","end":{"date-parts":[[2021,10,1]]}},"container-title":["2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9635848\/9635849\/09636651.pdf?arnumber=9636651","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:02:54Z","timestamp":1733338974000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9636651\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/iros51168.2021.9636651","relation":{},"subject":[],"published":{"date-parts":[[2021,9,27]]}}}