{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T20:18:37Z","timestamp":1776716317490,"version":"3.51.2"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Facebook AI Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1109\/lra.2022.3143518","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T22:02:30Z","timestamp":1642543350000},"page":"2740-2747","source":"Crossref","is-referenced-by-count":42,"title":["Efficient and Interpretable Robot Manipulation With Graph Neural Networks"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1514-9353","authenticated-orcid":false,"given":"Yixin","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Austin S.","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Undersander","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshara","family":"Rai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Gnnexplainer: Generating explanations for graph neural networks","volume":"32","author":"Ying","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref2","article-title":"Learning visual predictive models of physics for playing billiards","author":"Fragkiadaki","year":"2015"},{"key":"ref3","first-page":"100","article-title":"Object-centric forward modeling for model predictive control","author":"Ye","year":"2020","journal-title":"Conf. Robot Learn."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17421"},{"key":"ref5","first-page":"979","article-title":"Graph-structured visual imitation","volume-title":"Proc. Conf. Robot Learn.","author":"Sieb","year":"2020"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00876"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197468"},{"key":"ref8","first-page":"740","article-title":"My house, my rules: Learning tidying preferences with graph neural networks","author":"Kapelyukh","year":"2022","journal-title":"Conf. Robot Learn."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-091420-084139"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2011.5980391"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1613\/jair.5575"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1129"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i06.6542"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7354286"},{"key":"ref15","article-title":"Habitat 2.0: Training home assistants to rearrange their habitat","volume":"34","author":"Szot","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1177\/02783649211004615"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341535"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919848837"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2894861"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487165"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2020.XVI.003"},{"key":"ref22","first-page":"955","article-title":"Learning value functions with relational state representations for guiding task-and-motion planning","volume-title":"Proc. Conf. Robot Learn.","author":"Kim","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989109"},{"key":"ref25","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018"},{"key":"ref26","article-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019","journal-title":"ICLR Workshop Representation Learn. Graphs Manifolds"},{"key":"ref27","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"ref29","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref30","article-title":"Gated graph sequence neural networks","author":"Li","year":"2016","journal-title":"4th Int. Conf. Learn. Representations"},{"key":"ref31","article-title":"Graph attention networks","author":"Velikovi","year":"2018","journal-title":"Int. Conf. Learn. Representations"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.09.023"},{"key":"ref33","article-title":"Pybullet, a python module for physics simulation for games, robotics and machine learning","author":"Coumans","year":"2016"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201311"},{"key":"ref35","article-title":"Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning","volume":"abs\/1910.11956","author":"Gupta","year":"2019","journal-title":"CoRR"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2018.xiv.049"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1707.06347"},{"key":"ref38","article-title":"Stable baselines3","volume-title":"GitHub repository","author":"Raffin","year":"2019"},{"key":"ref39","article-title":"Polymetis","author":"Lin","year":"2021"},{"key":"ref40","article-title":"Variational graph recurrent neural networks","volume":"32","author":"Hajiramezanali","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9647862\/09684675.pdf?arnumber=9684675","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:37:48Z","timestamp":1705185468000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9684675\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":40,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/lra.2022.3143518","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4]]}}}