{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:33:14Z","timestamp":1730255594836,"version":"3.28.0"},"reference-count":42,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"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\/icra48506.2021.9561484","type":"proceedings-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:28:35Z","timestamp":1634689715000},"page":"4237-4243","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Hanging Point Learning from Random Shape Generation and Physical Function Validation"],"prefix":"10.1109","author":[{"given":"Kosuke","family":"Takeuchi","sequence":"first","affiliation":[]},{"given":"Iori","family":"Yanokura","sequence":"additional","affiliation":[]},{"given":"Yohei","family":"Kakiuchi","sequence":"additional","affiliation":[]},{"given":"Kei","family":"Okada","sequence":"additional","affiliation":[]},{"given":"Masayuki","family":"Inaba","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"510","article-title":"The ycb object and model set: Towards common benchmarks for manipulation research","author":"calli","year":"2015","journal-title":"ICAR"},{"key":"ref38","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"ICLRE"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref32","first-page":"226231","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"ester","year":"1996","journal-title":"SIGKDD"},{"key":"ref31","article-title":"Softgym: Benchmarking deep reinforcement learning for deformable object manipulation","author":"lin","year":"2020","journal-title":"CoRL"},{"key":"ref30","first-page":"2016","article-title":"Pybullet, a python module for physics simulation for games, robotics and machine learning","author":"coumans","year":"0"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177703732"},{"key":"ref36","article-title":"Random erasing data augmentation","volume":"34","author":"zhong","year":"2017","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref10","article-title":"Shapenet: An information-rich 3d model repository","volume":"abs 1512 3012","author":"chang","year":"2015","journal-title":"CoRR"},{"key":"ref40","first-page":"75","article-title":"odel simplification using vertex-clustering","author":"low","year":"1997","journal-title":"I3D"},{"key":"ref11","first-page":"1912","article-title":"3d shapenets: A deep representation for volumetric shapes","author":"wu","year":"2015","journal-title":"CVPR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.95"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.308"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460950"},{"key":"ref15","article-title":"Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes","volume":"abs 1711 199","author":"xiang","year":"2017","journal-title":"CoRR"},{"key":"ref16","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 NIPS&#x2019;14"},{"key":"ref17","first-page":"82","article-title":"Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling","author":"wu","year":"2016","journal-title":"NIPS"},{"key":"ref18","article-title":"Improved adversarial systems for 3d object generation and reconstruction","volume":"abs 1707 9557","author":"smith","year":"2017","journal-title":"CoRR"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00025"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139369"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1117\/12.2513011"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"ref6","article-title":"Acquiring mechanical knowledge from 3d point clouds","author":"li","year":"2020","journal-title":"IROS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759429"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00334"},{"key":"ref8","article-title":"Playing for data: Ground truth from computer games","author":"richter","year":"2016","journal-title":"ECCV"},{"key":"ref7","article-title":"kpam: Keypoint affordances for category-level robotic manipulation","volume":"abs 1903 6684","author":"manuelli","year":"2019","journal-title":"CoRR"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364907087172"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364914549607"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.146"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref22","article-title":"Learning visual feature spaces for robotic manipulation with deep spatial autoencoders","author":"finn","year":"2016","journal-title":"ICRA"},{"key":"ref21","first-page":"3304","article-title":"Efficient grasping from rgbd images: Learning using a new rectangle representation","author":"jiang","year":"2011","journal-title":"ICRA"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995327"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995448"},{"key":"ref41","first-page":"21","article-title":"Ssd: Single shot multibox detector","author":"liu","year":"2016","journal-title":"ECCV Vol 9905 of Lecture Notes in Computer Science"},{"key":"ref26","article-title":"Gan augmentation: Augmenting training data using generative adversarial networks","volume":"abs 1810 10863","author":"bowles","year":"2018","journal-title":"ArXiv"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2013.6696750"}],"event":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2021,5,30]]},"location":"Xi'an, China","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\/09561484.pdf?arnumber=9561484","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:25Z","timestamp":1652197645000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9561484\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/icra48506.2021.9561484","relation":{},"subject":[],"published":{"date-parts":[[2021,5,30]]}}}