{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:44:47Z","timestamp":1765547087623,"version":"3.37.3"},"reference-count":97,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01LM1372201"],"award-info":[{"award-number":["R01LM1372201"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CAREER1841569,TRIPODS1740735"],"award-info":[{"award-number":["CAREER1841569,TRIPODS1740735"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,29]]},"DOI":"10.1109\/icra48891.2023.10160948","type":"proceedings-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T17:20:56Z","timestamp":1688491256000},"page":"9068-9075","source":"Crossref","is-referenced-by-count":3,"title":["EMS\u00ae: A Massive Computational Experiment Management System towards Data-driven Robotics"],"prefix":"10.1109","author":[{"given":"Qinjie","family":"Lin","sequence":"first","affiliation":[{"name":"Northwestern University,Department of Computer Science,Evanston,IL,USA"}]},{"given":"Guo","family":"Ye","sequence":"additional","affiliation":[{"name":"Northwestern University,Department of Computer Science,Evanston,IL,USA"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwestern University,Department of Computer Science,Evanston,IL,USA"}]}],"member":"263","reference":[{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3311790.3396664"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098021"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.3390\/robotics7030047"},{"journal-title":"Scikit-Learn Machine Learning Simplified Implement Scikit-Learn Into Every Step of the Data Science Pipeline","year":"2017","author":"garreta","key":"ref58"},{"journal-title":"K8s batch","year":"0","key":"ref53"},{"key":"ref52","article-title":"Ambitious data science can be painless","author":"monajemi","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457566"},{"journal-title":"AWS Batch","year":"0","key":"ref54"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-4af1f417-001"},{"key":"ref50","first-page":"1789","article-title":"Roboflow: a data-centric workflow management system for developing ai-enhanced robots","author":"lin","year":"2022","journal-title":"Conference on Robot Learning"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-4470-8_2"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26622-6_22"},{"key":"ref48","first-page":"561","article-title":"Ray: A distributed framework for emerging {AI} applications","author":"moritz","year":"2018","journal-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-4470-8_46"},{"journal-title":"Codalab worksheets","year":"0","key":"ref42"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840870"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26622-6_24"},{"journal-title":"pywren","year":"0","key":"ref43"},{"key":"ref49","first-page":"1235","article-title":"Mllib: Machine learning in apache spark","volume":"17","author":"meng","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.010"},{"key":"ref7","article-title":"Emergence of locomotion behaviours in rich environments","author":"heess","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560837"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341361"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aau5872"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2979660"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561639"},{"key":"ref40","first-page":"2284","article-title":"Feedback-based tree search for reinforcement learning","author":"jiang","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref35","article-title":"Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter","author":"sanh","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref34","article-title":"Roberta: A robustly optimized bert pretraining approach","author":"liu","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref37","article-title":"Playing atari with deep reinforcement learning","author":"mnih","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref36","article-title":"Dota 2 with large scale deep reinforcement learning","author":"berner","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-020-09548-1"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10796-8"},{"journal-title":"Improving language understanding by generative pre-training","year":"2018","author":"radford","key":"ref33"},{"key":"ref32","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref39","article-title":"Starcraft ii unplugged: Large scale offline reinforcement learning","author":"mathieu","year":"0","journal-title":"Deep RL Workshop NeurIPS 2021"},{"key":"ref38","article-title":"Alphastar: mastering the real-time strategy game starcraft ii","volume":"24","author":"team","year":"2019","journal-title":"DeepMind blog"},{"key":"ref24","article-title":"One thousand and one hours: Self-driving motion prediction dataset","author":"houston","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref23","article-title":"Large batch simulation for deep reinforcement learning","author":"shacklett","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196810"},{"key":"ref25","article-title":"Deep reinforcement learning for real autonomous mobile robot navigation in indoor environments","author":"surmann","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref20","first-page":"158","article-title":"Implicit behavioral cloning","author":"florence","year":"2022","journal-title":"Conference on Robot Learning"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460655"},{"journal-title":"Language-conditioned policy learning for long-horizon robot manipulation tasks","year":"0","author":"mees","key":"ref21"},{"key":"ref28","article-title":"Semi-parametric topological memory for navigation","author":"savinov","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00063"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.769"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"ref15","first-page":"1101","article-title":"Deep dynamics models for learning dexterous manipulation","author":"nagabandi","year":"2020","journal-title":"Conference on Robot Learning"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487342"},{"key":"ref97","article-title":"Ray rllib: A composable and scalable reinforcement learning library","volume":"85","author":"liang","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461113"},{"key":"ref11","article-title":"Learning agile robotic locomotion skills by imitating animals","author":"peng","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"Multi-agent manipulation via locomotion using hierarchical sim2real","author":"nachum","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196619"},{"key":"ref16","first-page":"1486","article-title":"Guiding multi-step rearrangement tasks with natural language instructions","author":"stengel-eskin","year":"2022","journal-title":"Conference on Robot Learning"},{"key":"ref19","first-page":"3307","author":"bateux","year":"2018","journal-title":"Training Deep Neural Networks for Visual Servoing"},{"key":"ref18","article-title":"Learning visual servoing with deep features and fitted q-iteration","author":"lee","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1177\/1059712312462904"},{"key":"ref92","article-title":"Learning to plan in high dimensions via neural exploration-exploitation trees","author":"chen","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref95","article-title":"Pybullet a python module for physics simulation for games","author":"erwin","year":"2016","journal-title":"Pybullet"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2008.4650728"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919890396"},{"journal-title":"Automated Construction of Robotic Manipulation Programs","year":"2010","author":"diankov","key":"ref90"},{"key":"ref89","first-page":"170","article-title":"Turtlebot 3 as a robotics education platform","author":"amsters","year":"2019","journal-title":"International Conference on Robotics in Education (RiE)"},{"journal-title":"Minimalistic grid-world environment for openai gym","year":"2018","author":"chevalier-boisvert","key":"ref86"},{"key":"ref85","article-title":"Decision transformer: Reinforcement learning via sequence modeling","volume":"34","author":"chen","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-008-0014-4"},{"key":"ref87","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2009.94"},{"journal-title":"Aws outposts","year":"0","key":"ref81"},{"journal-title":"Boto 3 documentation","year":"2018","author":"garnaat","key":"ref84"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/CISTI.2014.6876862"},{"journal-title":"Azure arc","year":"0","key":"ref80"},{"journal-title":"Google anthos","year":"0","key":"ref79"},{"journal-title":"Mastering Kubernetes","year":"2017","author":"sayfan","key":"ref78"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-5781-4_3"},{"journal-title":"Confluent","year":"0","key":"ref74"},{"journal-title":"Trifacta","year":"0","key":"ref77"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2903741"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of go with deep neural networks and tree search","volume":"529","author":"silver","year":"2016","journal-title":"Nature"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919887447"},{"journal-title":"A smart and interactive edge-cloud big data system","year":"2022","author":"stauffer","key":"ref71"},{"key":"ref70","article-title":"Fogros 2: An adaptive and extensible platform for cloud and fog robotics using ros 2","author":"ichnowski","year":"2022","journal-title":"ArXiv Preprint"},{"journal-title":"AWS RoboMaker","year":"0","key":"ref73"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561824"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630612"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2011.941632"},{"key":"ref69","article-title":"Fogros: An adaptive framework for automating fog robotics deployment","author":"liang","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509469"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.23919\/IConAC.2019.8895254"},{"journal-title":"Formant robotics","year":"0","key":"ref66"},{"journal-title":"Fetch robotics","year":"0","key":"ref65"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2014.2376492"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.36548\/jucct.2020.1.002"},{"journal-title":"Cloud Robotics and Automation A Survey of Related Work","year":"2013","author":"goldberg","key":"ref61"}],"event":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2023,5,29]]},"location":"London, United Kingdom","end":{"date-parts":[[2023,6,2]]}},"container-title":["2023 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10160211\/10160212\/10160948.pdf?arnumber=10160948","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T17:31:46Z","timestamp":1690219906000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10160948\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":97,"URL":"https:\/\/doi.org\/10.1109\/icra48891.2023.10160948","relation":{},"subject":[],"published":{"date-parts":[[2023,5,29]]}}}