{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:38:44Z","timestamp":1743115124512,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616151"},{"type":"electronic","value":"9783030616168"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61616-8_44","type":"book-chapter","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T23:07:42Z","timestamp":1602889662000},"page":"546-555","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Geometries to Contact Graphs"],"prefix":"10.1007","author":[{"given":"Martin","family":"Meier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Haschke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helge J.","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Anders, A.S., Kaelbling, L.P., Lozano-Perez, T.: Reliably arranging objects in uncertain domains. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1603\u20131610. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462892"},{"key":"44_CR2","unstructured":"Battaglia, P., et al.: Interaction networks for learning about objects, relations and physics. In: Advances in neural information processing systems, pp. 4502\u20134510 (2016)"},{"key":"44_CR3","doi-asserted-by":"publisher","unstructured":"Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. pp. 875\u2013886. Springer, Boston, MA (2009). https:\/\/doi.org\/10.1007\/978-0-387-09823-4_45","DOI":"10.1007\/978-0-387-09823-4_45"},{"issue":"1","key":"44_CR4","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1109\/LRA.2019.2949221","volume":"5","author":"CR Dreher","year":"2019","unstructured":"Dreher, C.R., W\u00e4chter, M., Asfour, T.: Learning object-action relations from bimanual human demonstration using graph networks. IEEE Robot. Autom. Lett. 5(1), 187\u2013194 (2019)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"44_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"44_CR6","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). 3, pp. 2149\u20132154. IEEE (2004)","DOI":"10.1109\/IROS.2004.1389727"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"Nov","key":"44_CR9","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"44_CR10","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1109\/TMECH.2018.2801568","volume":"23","author":"E Najafi","year":"2018","unstructured":"Najafi, E., Shah, A., Lopes, G.A.: Robot contact language for manipulation planning. IEEE\/ASME Trans. Mechatron. 23(3), 1171\u20131181 (2018)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"44_CR11","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems, pp. 5099\u20135108 (2017)"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Rashid, M., Kjellstrom, H., Lee, Y.J.: Action graphs: weakly-supervised action localization with graph convolution networks. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 615\u2013624 (2020)","DOI":"10.1109\/WACV45572.2020.9093404"},{"issue":"11","key":"44_CR13","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1177\/0278364911408155","volume":"30","author":"B Rosman","year":"2011","unstructured":"Rosman, B., Ramamoorthy, S.: Learning spatial relationships between objects. Int. J. Robot. Res. 30(11), 1328\u20131342 (2011)","journal-title":"Int. J. Robot. Res."},{"key":"44_CR14","unstructured":"Sanchez-Gonzalez, A., et al.: Graph networks as learnable physics engines for inference and control. In: International Conference on Machine Learning, pp. 4470\u20134479 (2018)"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Scherzinger, S., Roennau, A., Dillmann, R.: Contact skill imitation learning for robot-independent assembly programming. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4309\u20134316. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967523"},{"key":"44_CR16","unstructured":"Smith, S.L., Kindermans, P.J., Ying, C., Le, Q.V.: Don\u2019t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, L., Wang, L., Guo, J., Wu, G.: Learning actor relation graphs for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9964\u20139974. IEEE (2019)","DOI":"10.1109\/CVPR.2019.01020"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61616-8_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T01:04:36Z","timestamp":1723770276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61616-8_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616151","9783030616168"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61616-8_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"249","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"139","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"*The conference was postponed to 2021 due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}