{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T19:20:30Z","timestamp":1776194430238,"version":"3.50.1"},"reference-count":117,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"79","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2023,6,28]]},"abstract":"<jats:p>Semantic navigation is necessary to deploy mobile robots in uncontrolled environments such as homes or hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, whereas modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. However, learned visual navigation policies have predominantly been evaluated in sim, with little known about what works on a robot. We present a large-scale empirical study of semantic visual navigation methods comparing representative methods with classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We found that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% sim to a 23% real-world success rate because of a large image domain gap between sim and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: Modularity and abstraction in policy design enable sim-to-real transfer. For researchers, we identify two key issues that prevent today\u2019s simulators from being reliable evaluation benchmarks\u2014a large sim-to-real gap in images and a disconnect between sim and real-world error modes\u2014and propose concrete steps forward.<\/jats:p>","DOI":"10.1126\/scirobotics.adf6991","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T17:58:50Z","timestamp":1687975130000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":108,"title":["Navigating to objects in the real world"],"prefix":"10.1126","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8851-533X","authenticated-orcid":true,"given":"Theophile","family":"Gervet","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}]},{"given":"Soumith","family":"Chintala","sequence":"additional","affiliation":[{"name":"Meta AI Research, Menlo Park, CA, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1358-0011","authenticated-orcid":true,"given":"Dhruv","family":"Batra","sequence":"additional","affiliation":[{"name":"Meta AI Research, Menlo Park, CA, USA."},{"name":"Georgia Institute of Technology, Atlanta, GA, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3695-1580","authenticated-orcid":true,"given":"Jitendra","family":"Malik","sequence":"additional","affiliation":[{"name":"Meta AI Research, Menlo Park, CA, USA."},{"name":"University of California, Berkeley, CA, USA."}]},{"given":"Devendra Singh","family":"Chaplot","sequence":"additional","affiliation":[{"name":"Meta AI Research, Menlo Park, CA, USA."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","unstructured":"P. Anderson A.\u00a0Chang D.\u00a0S.\u00a0Chaplot A.\u00a0Dosovitskiy S.\u00a0Gupta V.\u00a0Koltun J.\u00a0Kosecka J.\u00a0Malik R.\u00a0Mottaghi M.\u00a0Savva A.\u00a0R.\u00a0Zamir On evaluation of embodied navigation agents. arXiv:1807.06757 [cs.AI] (18 July 2018)."},{"key":"e_1_3_2_3_2","unstructured":"H.\u00a0P.\u00a0Moravec Obstacle avoidance and navigation in the real world by a seeing robot rover thesis Stanford University Palo Alto CA (1980)."},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"R. Chatila J.-P. Laumond \u201cPosition referencing and consistent world modeling for mobile robots \u201d in Proceedings of the 1985 IEEE International Conference on Robotics and Automation St.\u00a0Louis MO 25 to 28 March 1985 (IEEE 1985); vol. 2 pp.\u00a0138\u2013145.","DOI":"10.1109\/ROBOT.1985.1087373"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/JRA.1987.1087096"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/2.30720"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/0921-8890(91)90014-C"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.75902"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/027836499201100402"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/100.580977"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(99)00070-3"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"S. Thrun M.\u00a0Bennewitz W.\u00a0Burgard A.\u00a0B.\u00a0Cremers F.\u00a0Dellaert D.\u00a0Fox D.\u00a0Hahnel C.\u00a0Rosenberg N.\u00a0Roy J.\u00a0Schulte D.\u00a0Schulz \u201cMINERVA: A second-generation museum tour-guide robot \u201d in Proceedings 1999 IEEE International Conference on Robotics and Automation Detroit MI 10 to 15 May 1999 (IEEE 1999); vol. 3.","DOI":"10.1109\/ROBOT.1999.770401"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(01)00069-8"},{"key":"e_1_3_2_14_2","unstructured":"S. Thrun Robotic mapping: A survey in Exploring Artificial Intelligence in the New Millennium (Morgan Kaufmann Publishers Inc. 2002) pp.\u00a01\u201335."},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"R.\u00a0A.\u00a0Newcombe S.\u00a0Izadi O.\u00a0Hilliges D.\u00a0Molyneaux D.\u00a0Kim A.\u00a0J.\u00a0Davison P.\u00a0Kohi J.\u00a0Shotton S.\u00a0Hodges A.\u00a0Fitzgibbon \u201cKinectfusion: Real-time dense surface mapping and tracking \u201d in Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality Basel Switzerland 26 to 29 October 2011 (IEEE 2011) pp.\u00a0127\u2013136.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1049"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364910388963"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"T. Sattler W.\u00a0Maddern C.\u00a0Toft A.\u00a0Torii L.\u00a0Hammarstrand E.\u00a0Stenborg D.\u00a0Safari M.\u00a0Okutomi M.\u00a0Pollefeys J.\u00a0Sivic F.\u00a0Kahl T.\u00a0Pajdla \u201cBenchmarking 6dof outdoor visual localization in changing conditions \u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Salt Lake City UT 18 to 23 June 2018 (IEEE 2018) pp.\u00a08601\u20138610.","DOI":"10.1109\/CVPR.2018.00897"},{"key":"e_1_3_2_19_2","unstructured":"B. Yamauchi \u201cA frontier-based approach for autonomous exploration \u201d in Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation ' Monterey CA 10 to 11 July 1997 (IEEE 1997) pp.\u00a0146\u2013151."},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"A. Flint D.\u00a0Murray I.\u00a0Reid \u201cManhattan scene understanding using monocular stereo and 3d features \u201d in Proceedings of the 2011 International Conference on Computer Vision Barcelona Spain 6 to 13 November 2011 (IEEE 2011) pp.\u00a02228\u20132235.","DOI":"10.1109\/ICCV.2011.6126501"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"A. Kundu Y.\u00a0Li F.\u00a0Dellaert F.\u00a0Li J.\u00a0M.\u00a0Rehg Joint semantic segmentation and 3d reconstruction from monocular video in European Conference on Computer Vision (Springer 2014) pp.\u00a0703\u2013718.","DOI":"10.1007\/978-3-319-10599-4_45"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"S.\u00a0L.\u00a0Bowman N.\u00a0Atanasov K.\u00a0Daniilidis G.\u00a0J.\u00a0Pappas \u201cProbabilistic data association for semantic SLAM \u201d in Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) Singapore 29 May to 3 June 2017 (IEEE 2017) pp.\u00a01722\u20131729.","DOI":"10.1109\/ICRA.2017.7989203"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"L. Ma J.\u00a0St\u00fcckler C.\u00a0Kerl D.\u00a0Cremers \u201cMulti-view deep learning for consistent semantic mapping with RGB-D cameras \u201d in Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2017) pp.\u00a0598\u2013605.","DOI":"10.1109\/IROS.2017.8202213"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2873617"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"A. Rosinol M.\u00a0Abate Y.\u00a0Chang L.\u00a0Carlone \u201cKimera: An open-source library for real-time metric-semantic localization and mapping \u201d in Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) Paris France 31 May to 31 August 2020 (IEEE 2020) pp.\u00a01689\u20131696.","DOI":"10.1109\/ICRA40945.2020.9196885"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"R.\u00a0F.\u00a0Salas-Moreno R.\u00a0A.\u00a0Newcombe H.\u00a0Strasdat P.\u00a0H.\u00a0Kelly A.\u00a0J.\u00a0Davison \u201cSlam++: Simultaneous localisation and mapping at the level of objects \u201d in Proceedings of the IEEE conference on computer vision and pattern recognition Portland OR 23 to 28 June 2013 (IEEE 2013) pp.\u00a01352\u20131359.","DOI":"10.1109\/CVPR.2013.178"},{"key":"e_1_3_2_27_2","unstructured":"D.\u00a0A.\u00a0Pomerleau Alvinn: An autonomous land vehicle in a neural network in Advances in Neural Information Processing Systems (Morgan Kaufmann Publishers Inc. 1988) vol. 1."},{"key":"e_1_3_2_28_2","unstructured":"U. Muller J.\u00a0Ben E.\u00a0Cosatto B.\u00a0Flepp Y.\u00a0Cun Off-road obstacle avoidance through end-to-end learning in Advances in Neural Information Processing Systems (MIT Press 2005) vol. 18."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_30_2","unstructured":"T.\u00a0P.\u00a0Lillicrap J.\u00a0J.\u00a0Hunt A.\u00a0Pritzel N.\u00a0Heess T.\u00a0Erez Y.\u00a0Tassa D.\u00a0Silver D.\u00a0Wierstra Continuous control with deep reinforcement learning. arXiv:1509.02971 [cs.LG] (9 September 2015)."},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"G. Lample D.\u00a0S.\u00a0Chaplot Playing FPS games with deep reinforcement learning in The Thirty-First AAAI Conference on Artificial Intelligence (AAAI) (AAAI 2017); 10.1609\/aaai.v31i1.10827.","DOI":"10.1609\/aaai.v31i1.10827"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Y. Zhu R.\u00a0Mottaghi E.\u00a0Kolve J.\u00a0J.\u00a0Lim A.\u00a0Gupta L.\u00a0Fei-Fei A.\u00a0Farhadi \u201cTarget-driven visual navigation in indoor scenes using deep reinforcement learning \u201d in Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) Singapore 29 May to 03 June 2017 (IEEE 2017) pp.\u00a03357\u20133364.","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"e_1_3_2_33_2","unstructured":"P. Mirowski R.\u00a0Pascanu F.\u00a0Viola H.\u00a0Soyer A.\u00a0Ballard A.\u00a0Banino M.\u00a0Denil R.\u00a0Goroshin L.\u00a0Sifre K.\u00a0Kavukcuoglu D.\u00a0Kumaran R.\u00a0Hadsell Learning to navigate in complex environments \u201d paper presented at the 5th International Conference on Learning Representations Toulon France 24 to 26 2017 (ICLR 2017)."},{"key":"e_1_3_2_34_2","unstructured":"A. Dosovitskiy V.\u00a0Koltun \u201cLearning to act by predicting the future \u201d paper presented at the 5th International Conference on Learning Representations Toulon France 24 to 26 2017 (ICLR 2017)."},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"D.\u00a0S.\u00a0Chaplot G.\u00a0Lample \u201cArnold: An autonomous agent to play fps games \u201d in The Thirty-First AAAI Conference on Artificial Intelligence (AAAI) (AAAI 2017).","DOI":"10.1609\/aaai.v31i1.10534"},{"key":"e_1_3_2_36_2","unstructured":"M. Savva A.\u00a0X.\u00a0Chang A.\u00a0Dosovitskiy T.\u00a0Funkhouser V.\u00a0Koltun MINOS: Multimodal indoor simulator for navigation in complex environments. arXiv:1712.03931 [cs.LG] (11 December 2017)."},{"key":"e_1_3_2_37_2","unstructured":"K.\u00a0M.\u00a0Hermann F.\u00a0Hill S.\u00a0Green F.\u00a0Wang R.\u00a0Faulkner H.\u00a0Soyer D.\u00a0Szepesvari W.\u00a0M.\u00a0Czarnecki M.\u00a0Jaderberg D.\u00a0Teplyashin M.\u00a0Wainwright C.\u00a0Apps D.\u00a0Hassabis P.\u00a0Blunsom Grounded language learning in a simulated 3D world. arXiv:1706.06551 [cs.CL] (20 June 2017)."},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","unstructured":"D.\u00a0S.\u00a0Chaplot K.\u00a0M.\u00a0Sathyendra R.\u00a0K.\u00a0Pasumarthi D.\u00a0Rajagopal R.\u00a0Salakhutdinov Gated-attention architectures for task-oriented language grounding. arXiv:1706.07230 [cs.LG] (22 June 2017).","DOI":"10.1609\/aaai.v32i1.11832"},{"key":"e_1_3_2_39_2","unstructured":"P. Mirowski M.\u00a0K.\u00a0Grimes M.\u00a0Malinowski K.\u00a0M.\u00a0Hermann K.\u00a0Anderson D.\u00a0Teplyashin K.\u00a0Simonyan K.\u00a0Kavukcuoglu A.\u00a0Zisserman R.\u00a0Hadsell Learning to navigate in cities without a map in Advances in Neural Information Processing Systems (Curran Associates Inc. 2018) vol. 31 pp.\u00a02419\u20132430."},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"F. Codevilla M.\u00a0M\u00fcller A.\u00a0L\u00f3pez V.\u00a0Koltun A.\u00a0Dosovitskiy \u201cEnd-to-end driving via conditional imitation learning \u201d in Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) Brisbane Queensland Australia 21 to 25 May 2018 (IEEE 2018) pp.\u00a04693\u20134700.","DOI":"10.1109\/ICRA.2018.8460487"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0102-6"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","unstructured":"J. Ye D.\u00a0Batra A.\u00a0Das E.\u00a0Wijmans \u201cAuxiliary tasks and exploration enable objectgoal navigation \u201d in Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Montreal Quebec Canada 10 to 17 October 2021 (IEEE 2021) pp.\u00a016117\u201316126.","DOI":"10.1109\/ICCV48922.2021.01581"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","unstructured":"O. Maksymets V.\u00a0Cartillier A.\u00a0Gokaslan E.\u00a0Wijmans W.\u00a0Galuba S.\u00a0Lee D.\u00a0Batra \u201cTHDA: Treasure hunt data augmentation for semantic navigation \u201d in Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Montreal Quebec Canada 10 to 17 October 2021 (IEEE 2021) pp.\u00a015374\u201315383.","DOI":"10.1109\/ICCV48922.2021.01509"},{"key":"e_1_3_2_44_2","doi-asserted-by":"crossref","unstructured":"R. Ramrakhya E.\u00a0Undersander D.\u00a0Batra A.\u00a0Das \u201cHabitat-Web: Learning embodied object-search strategies from human demonstrations at scale \u201d in Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) New Orleans LA 18-24 June 2022 (IEEE 2022) pp.\u00a05173\u20135183.","DOI":"10.1109\/CVPR52688.2022.00511"},{"key":"e_1_3_2_45_2","unstructured":"E. Wijmans A.\u00a0Kadian A.\u00a0Morcos S.\u00a0Lee I.\u00a0Essa D.\u00a0Parikh M.\u00a0Savva D.\u00a0Batra DD-PPO: Learning near-perfect PointGoal navigators from 2.5 billion frames. arXiv:1911.00357 [cs.CV] (1 November 2019)."},{"key":"e_1_3_2_46_2","unstructured":"M. Deitke E.\u00a0V.\u00a0Bilt A.\u00a0Herrasti L.\u00a0Weihs J.\u00a0Salvador K.\u00a0Ehsani W.\u00a0Han E.\u00a0Kolve A.\u00a0Farhadi A.\u00a0Kembhavi R.\u00a0Mottaghi ProcTHOR: Large-scale embodied AI using procedural generation. arXiv:2206.06994 [cs.AI] (14 June 2022)."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/JRA.1986.1087032"},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","unstructured":"S. Gupta J.\u00a0Davidson S.\u00a0Levine R.\u00a0Sukthankar J.\u00a0Malik \u201cCognitive mapping and planning for visual navigation \u201d in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu HI 21 to 26 July 2017 (IEEE 2017) pp.\u00a02616\u20132625.","DOI":"10.1109\/CVPR.2017.769"},{"key":"e_1_3_2_49_2","unstructured":"E. Parisotto R.\u00a0Salakhutdinov Neural map: Structured memory for deep reinforcement learning in International Conference on Learning Representations (ICLR) (2018)."},{"key":"e_1_3_2_50_2","unstructured":"D.\u00a0S.\u00a0Chaplot E.\u00a0Parisotto R.\u00a0Salakhutdinov Active neural localization in International Conference on Learning Representations (ICLR) (2018)."},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","unstructured":"J.\u00a0F.\u00a0Henriques A.\u00a0Vedaldi \u201cMapnet: An allocentric spatial memory for mapping environments \u201d in Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Salt Lake City UT 18 to 23 June 2018 (IEEE 2018) pp.\u00a08476\u20138484.","DOI":"10.1109\/CVPR.2018.00884"},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","unstructured":"D. Gordon A.\u00a0Kembhavi M.\u00a0Rastegari J.\u00a0Redmon D.\u00a0Fox A.\u00a0Farhadi \u201cIqa: Visual question answering in interactive environments \u201d in Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Salt Lake City UT 18 to 23 June 2018 (IEEE 2018) pp.\u00a04089\u20134098.","DOI":"10.1109\/CVPR.2018.00430"},{"key":"e_1_3_2_53_2","unstructured":"W. Yang X.\u00a0Wang A.\u00a0Farhadi A.\u00a0Gupta R.\u00a0Mottaghi Visual semantic navigation using scene priors. arXiv:1810.06543 [cs.CV] (15 October 2018)."},{"key":"e_1_3_2_54_2","unstructured":"N. Savinov A.\u00a0Dosovitskiy V.\u00a0Koltun \u201cSemi-parametric topological memory for navigation \u201d paper presented at the 6th International Conference on Learning Representations (ICLR 2018) Vancouver British Columbia Canada 30 April to 3 May 2018."},{"key":"e_1_3_2_55_2","unstructured":"N. Savinov A.\u00a0Raichuk D.\u00a0Vincent R.\u00a0Marinier M.\u00a0Pollefeys T.\u00a0P.\u00a0Lillicrap S.\u00a0Gelly \u201cEpisodic curiosity through reachability \u201d paper presented at the 7th International Conference on Learning Representations (ICLR 2019) New Orleans LA 6 to 9 May 2019."},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"T. Campari L.\u00a0Lamanna P.\u00a0Traverso L.\u00a0Serafini L.\u00a0Ballan \u201cOnline learning of reusable abstract models for object goal navigation \u201d in Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) New Orleans LA 18 to 24 June 2022 (IEEE 2022) pp.\u00a014870\u201314879.","DOI":"10.1109\/CVPR52688.2022.01445"},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","unstructured":"R. McAllister Y.\u00a0Gal A.\u00a0Kendall M.\u00a0van der Wilk A.\u00a0Shah R.\u00a0Cipolla A.\u00a0Weller \u201cConcrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning \u201d paper presented at the Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Melbourne Australia 19 to 25 August 2017.","DOI":"10.24963\/ijcai.2017\/661"},{"key":"e_1_3_2_58_2","unstructured":"M. M\u00fcller A.\u00a0Dosovitskiy B.\u00a0Ghanem V.\u00a0Koltun Driving policy transfer via modularity and abstraction. arXiv:1804.09364 [cs.RO] (25 April 2018)."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2014.2322295"},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","unstructured":"A. Mousavian C.\u00a0Eppner D.\u00a0Fox \u201c6-dof graspnet: Variational grasp generation for object manipulation \u201d in Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) Seoul South Korea 27 October\u20132 November 2019 (IEEE 2019) pp.\u00a02901\u20132910.","DOI":"10.1109\/ICCV.2019.00299"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","unstructured":"J. Mahler J.\u00a0Liang S.\u00a0Niyaz M.\u00a0Laskey R.\u00a0Doan X.\u00a0Liu J.\u00a0A.\u00a0Ojea K.\u00a0Goldberg Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. arXiv:1703.09312 [cs.RO] (27 March 2017).","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","unstructured":"D. Morrison A.\u00a0W.\u00a0Tow M.\u00a0McTaggart R.\u00a0Smith N.\u00a0Kelly-Boxall S.\u00a0Wade-Mc Cue J.\u00a0Erskine R.\u00a0Grinover A.\u00a0Gurman T.\u00a0Hunn D.\u00a0Lee A.\u00a0Milan T.\u00a0Pham G.\u00a0Rallos A.\u00a0Razjigaev T.\u00a0Rowntree K.\u00a0Vijay Z.\u00a0Zhuang C.\u00a0Lehnert I.\u00a0Reid P.\u00a0Corke J.\u00a0Leitner \u201cCartman: The low-cost cartesian manipulator that won the amazon robotics challenge \u201d in Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) Brisbane Queensland Australia 21 to 25 May 2018 (IEEE 2018) pp.\u00a07757\u20137764.","DOI":"10.1109\/ICRA.2018.8463191"},{"key":"e_1_3_2_63_2","unstructured":"D.\u00a0S.\u00a0Chaplot D.\u00a0Gandhi S.\u00a0Gupta A.\u00a0Gupta R.\u00a0Salakhutdinov \u201cLearning To Explore Using Active Neural SLAM \u201d paper presented at the 8th International Conference on Learning Representations (ICLR 2020) Addis Ababa Ethiopia 26 to 30 April 2020."},{"key":"e_1_3_2_64_2","unstructured":"D.\u00a0S.\u00a0Chaplot D.\u00a0P.\u00a0Gandhi A.\u00a0Gupta R.\u00a0R.\u00a0Salakhutdinov Object goal navigation using goal-oriented semantic exploration in Advances in Neural Information Processing Systems (Curran Associates Inc. 2020) vol. 33 p.\u00a04247."},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","unstructured":"S.\u00a0K.\u00a0Ramakrishnan Z.\u00a0Al-Halah K.\u00a0Grauman Occupancy anticipation for efficient exploration and navigation in European Conference on Computer Vision (Springer 2020) pp.\u00a0400\u2013418.","DOI":"10.1007\/978-3-030-58558-7_24"},{"key":"e_1_3_2_66_2","unstructured":"D.\u00a0S.\u00a0Chaplot R.\u00a0Salakhutdinov A.\u00a0Gupta S.\u00a0Gupta \u201cNeural topological SLAM for visual navigation \u201d paper presented at the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Seattle WA 13 to 19 June 2020."},{"key":"e_1_3_2_67_2","doi-asserted-by":"crossref","unstructured":"S.\u00a0K.\u00a0Ramakrishnan D.\u00a0S.\u00a0Chaplot Z.\u00a0Al-Halah J.\u00a0Malik K.\u00a0Grauman PONI: Potential functions for ObjectGoal navigation with interaction-free learning in 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2022).","DOI":"10.1109\/CVPR52688.2022.01832"},{"key":"e_1_3_2_68_2","unstructured":"M. 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Interactions 9, 17 (2022).","journal-title":"Interactions"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3178804"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","unstructured":"G. Sarch Z.\u00a0Fang A.\u00a0W.\u00a0Harley P.\u00a0Schydlo M.\u00a0J.\u00a0Tarr S.\u00a0Gupta K.\u00a0Fragkiadaki \u201cTIDEE: Tidying up novel rooms using visuo-semantic commonsense priors \u201d paper presented at the 17th European Conference on Computer Vision (ECCV 2022) Tel Aviv Israel 23 to 27 October 2022.","DOI":"10.1007\/978-3-031-19842-7_28"},{"key":"e_1_3_2_75_2","unstructured":"B. Trabucco G.\u00a0Sigurdsson R.\u00a0Piramuthu G.\u00a0S.\u00a0Sukhatme R.\u00a0Salakhutdinov A simple approach for visual rearrangement: 3D mapping and semantic search. arXiv:2206.13396 [cs.CV] (21 June 2022)."},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","unstructured":"D.\u00a0S.\u00a0Chaplot H.\u00a0Jiang S.\u00a0Gupta A.\u00a0Gupta Semantic Curiosity for Active Visual Learning paper presented at the Computer Vision \u2013 ECCV 2020: 16th European Conference Glasgow UK 23 to 28 August 2020.","DOI":"10.1007\/978-3-030-58539-6_19"},{"key":"e_1_3_2_77_2","unstructured":"D.\u00a0S.\u00a0Chaplot M.\u00a0Dalal S.\u00a0Gupta J.\u00a0Malik R.\u00a0Salakhutdinov SEAL: Self-supervised Embodied Active Learning using exploration and 3D consistency in Advances in Neural Information Processing Systems (Curran Associates Inc. 2021)."},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3141105"},{"key":"e_1_3_2_79_2","unstructured":"D. Mishkin A.\u00a0Dosovitskiy V.\u00a0Koltun Benchmarking classic and learned navigation in complex 3D environments. arXiv:1901.10915 [cs.CV] (30 January 2019)."},{"key":"e_1_3_2_80_2","doi-asserted-by":"crossref","unstructured":"M. Savva Z.\u00a0Kira G.\u00a0A.\u00a0Regib J.\u00a0Yoo R.\u00a0Chen J.\u00a0Zheng \u201cHabitat: A platform for embodied AI research \u201d paper presented at the 2019 International Conference on Computer Vision (ICCV 2019) Seoul South Korea 27 October to 2 November 2019.","DOI":"10.1109\/ICCV.2019.00943"},{"key":"e_1_3_2_81_2","unstructured":"E. Kolve R.\u00a0Mottaghi W.\u00a0Han E.\u00a0V.\u00a0Bilt L.\u00a0Weihs A.\u00a0Herrasti M.\u00a0Deitke K.\u00a0Ehsani D.\u00a0Gordon Y.\u00a0Zhu A.\u00a0Kembhavi A.\u00a0Gupta A.\u00a0Farhadi AI2-THOR: An interactive 3D environment for visual AI. arXiv:1712.05474 [cs.CV] (26 August 2022)."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3013848"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3062303"},{"key":"e_1_3_2_84_2","unstructured":"J. Truong M.\u00a0Rudolph N.\u00a0Yokoyama S.\u00a0Chernova D.\u00a0Batra A.\u00a0Rai Rethinking Sim2Real: Lower fidelity simulation leads to higher Sim2Real transfer in navigation. arXiv:2207.10821 [cs.RO] (21 July 2022)."},{"key":"e_1_3_2_85_2","doi-asserted-by":"crossref","unstructured":"Z. Fu A.\u00a0Kumar A.\u00a0Agarwal H.\u00a0Qi J.\u00a0Malik D.\u00a0Pathak \u201cCoupling vision and proprioception for navigation of legged robots \u201d in Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) New Orleans LA 19 to 20 June 2022 (IEEE 2022) pp.\u00a017273\u201317283.","DOI":"10.1109\/CVPRW56347.2022.00508"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abk2822"},{"key":"e_1_3_2_87_2","doi-asserted-by":"crossref","unstructured":"R. Partsey E.\u00a0Wijmans N.\u00a0Yokoyama O.\u00a0Dobosevych D.\u00a0Batra O.\u00a0Maksymets \u201cIs mapping necessary for realistic PointGoal navigation? \u201d in Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) New Orleans LA 19 to 20 June 2022 (IEEE 2022) pp.\u00a017232\u201317241.","DOI":"10.1109\/CVPR52688.2022.01672"},{"key":"e_1_3_2_88_2","doi-asserted-by":"crossref","unstructured":"D. Shah B.\u00a0Eysenbach G.\u00a0Kahn N.\u00a0Rhinehart S.\u00a0Levine \u201cVing: Learning open-world navigation with visual goals \u201d in Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA) Xi'an China 30 May to 5 June 2021 (IEEE 2021) pp.\u00a013215\u201313222.","DOI":"10.1109\/ICRA48506.2021.9561936"},{"key":"e_1_3_2_89_2","doi-asserted-by":"crossref","unstructured":"D. Shah S.\u00a0Levine ViKiNG: Vision-based kilometer-scale navigation with geographic hints. arXiv:2202.11271 [cs.RO] (2022).","DOI":"10.15607\/RSS.2022.XVIII.019"},{"key":"e_1_3_2_90_2","doi-asserted-by":"crossref","unstructured":"P. Anderson Q.\u00a0Wu D.\u00a0Teney J.\u00a0Bruce M.\u00a0Johnson N.\u00a0S\u00fcnderhauf I.\u00a0Reid S.\u00a0Gould A.\u00a0van den Hengel \u201cVision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments \u201d in Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Salt Lake City UT 18 to 23 June 2018 (IEEE 2018) pp.\u00a03674\u20133683.","DOI":"10.1109\/CVPR.2018.00387"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2021.3064065"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abg5810"},{"key":"e_1_3_2_93_2","doi-asserted-by":"crossref","unstructured":"C.\u00a0C.\u00a0Kemp A.\u00a0Edsinger H.\u00a0M.\u00a0Clever B.\u00a0Matulevich \u201cThe design of Stretch: A compact lightweight mobile manipulator for indoor human environments \u201d in Proceedings of the 2022 International Conference on Robotics and Automation (ICRA) Philadelphia PA 23 to 27 May 2022 (IEEE 2022) pp.\u00a03150\u20133157.","DOI":"10.1109\/ICRA46639.2022.9811922"},{"key":"e_1_3_2_94_2","unstructured":"D. Batra A.\u00a0Gokaslan A.\u00a0Kembhavi O.\u00a0Maksymets R.\u00a0Mottaghi M.\u00a0Savva A.\u00a0Toshev E.\u00a0Wijmans ObjectNav revisited: On evaluation of embodied agents navigating to objects. arXiv:2006.13171 [cs.CV] (23 June 2020)."},{"key":"e_1_3_2_95_2","doi-asserted-by":"crossref","unstructured":"K. Yadav R.\u00a0Ramrakhya S.\u00a0K.\u00a0Ramakrishnan T.\u00a0Gervet J.\u00a0Turner A.\u00a0Gokaslan N.\u00a0Maestre A.\u00a0X.\u00a0Chang D.\u00a0Batra M.\u00a0Savva A.\u00a0W.\u00a0Clegg D.\u00a0S.\u00a0Chaplot Habitat-Matterport 3D semantics dataset. arXiv:2210.05633 [cs.CV] (11 October 2022).","DOI":"10.1109\/CVPR52729.2023.00477"},{"key":"e_1_3_2_96_2","unstructured":"S.\u00a0K.\u00a0Ramakrishnan A.\u00a0Gokaslan E.\u00a0Wijmans O.\u00a0Maksymets A.\u00a0Clegg J.\u00a0Turner E.\u00a0Undersander W.\u00a0Galuba A.\u00a0Westbury A.\u00a0X.\u00a0Chang M.\u00a0Savva Y.\u00a0Zhao D.\u00a0Batra Habitat-matterport 3D dataset (HM3D): 1000 large-scale 3D environments for embodied AI. arXiv:2109.08238 [cs.CV] (16 September 2021)."},{"key":"e_1_3_2_97_2","doi-asserted-by":"crossref","unstructured":"X. Zhou R.\u00a0Girdhar A.\u00a0Joulin P.\u00a0Kr\u00e4henb\u00fchl I.\u00a0Misra Detecting twenty-thousand classes using image-level supervision. arXiv:2201.02605 [cs.CV] (7 January 2022).","DOI":"10.1007\/978-3-031-20077-9_21"},{"key":"e_1_3_2_98_2","doi-asserted-by":"crossref","unstructured":"W. Li X.\u00a0Song Y.\u00a0Bai S.\u00a0Zhang S.\u00a0Jiang \u201cION: Instance-level Object Navigation \u201d in Proceedings of the 29th ACM International Conference on Multimedia Virtual Event China 20 to 24 October 2021 (Association for Computing Machinery 2021) pp.\u00a04343\u20134352.","DOI":"10.1145\/3474085.3475575"},{"key":"e_1_3_2_99_2","doi-asserted-by":"crossref","unstructured":"A. Khandelwal L.\u00a0Weihs R.\u00a0Mottaghi A.\u00a0Kembhavi \u201cSimple but effective: Clip embeddings for embodied AI \u201d in Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) New Orleans LA 18 to 24 June 2022 (IEEE 2022) pp.\u00a014809\u201314818.","DOI":"10.1109\/CVPR52688.2022.01441"},{"key":"e_1_3_2_100_2","doi-asserted-by":"crossref","unstructured":"K. Fang A.\u00a0Toshev L.\u00a0Fei-Fei S.\u00a0Savarese \u201cScene memory transformer for embodied agents in long-horizon tasks \u201d paper presented at the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Long Beach CA 15 to 20 June 2019.","DOI":"10.1109\/CVPR.2019.00063"},{"key":"e_1_3_2_101_2","doi-asserted-by":"crossref","unstructured":"M. Zhu B.\u00a0Zhao T.\u00a0Kong \u201cNavigating to objects in unseen environments by distance prediction \u201d paper presented at the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) Kyoto Japan 23 to 27 October 2022.","DOI":"10.1109\/IROS47612.2022.9981766"},{"key":"e_1_3_2_102_2","doi-asserted-by":"crossref","unstructured":"L. Pinto A.\u00a0Gupta \u201cSupersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours \u201d in Proceedings of the 2016 IEEE international conference on robotics and automation (ICRA) Stockholm Sweden 16 to 21 May 2016 (IEEE 2016) pp.\u00a03406\u20133413.","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"e_1_3_2_103_2","unstructured":"D. Kalashnikov A.\u00a0Irpan P.\u00a0Pastor J.\u00a0Ibarz A.\u00a0Herzog E.\u00a0Jang D.\u00a0Quillen E.\u00a0Holly M.\u00a0Kalakrishnan V.\u00a0Vanhoucke S.\u00a0Levine \u201cScalable deep reinforcement learning for vision-based robotic manipulation \u201d paper presented at the 2nd Annual Conference on Robot Learning (CoRL 2018) Z\u00fcrich Switzerland 29 to 31 October 2018 pp.\u00a0651\u2013673."},{"key":"e_1_3_2_104_2","doi-asserted-by":"crossref","unstructured":"J. Tobin R.\u00a0Fong A.\u00a0Ray J.\u00a0Schneider W.\u00a0Zaremba P.\u00a0Abbeel \u201cDomain randomization for transferring deep neural networks from simulation to the real world \u201d in Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) Vancouver British Columbia Canada 24 to 28 September 2017 (IEEE 2017) pp.\u00a023\u201330.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919887447"},{"key":"e_1_3_2_106_2","doi-asserted-by":"crossref","unstructured":"E. Kaufmann A.\u00a0Loquercio R.\u00a0Ranftl M.\u00a0M\u00fcller V.\u00a0Koltun D.\u00a0Scaramuzza Deep drone acrobatics. arXiv:2006.05768 [cs.RO] (10 June 2020).","DOI":"10.15607\/RSS.2020.XVI.040"},{"key":"e_1_3_2_107_2","unstructured":"A. Szot A.\u00a0Clegg E.\u00a0Undersander E.\u00a0Wijmans Y.\u00a0Zhao J.\u00a0Turner N.\u00a0Maestre M.\u00a0Mukadam D.\u00a0Chaplot O.\u00a0Maksymets A.\u00a0Gokaslan V.\u00a0Vondrus S.\u00a0Dharur F.\u00a0Meier W.\u00a0Galuba A.\u00a0Chang Z.\u00a0Kira V.\u00a0Koltun J.\u00a0Malik M.\u00a0Savva D.\u00a0Batra Habitat 2.0: Training home assistants to rearrange their habitat in Advances in Neural Information Processing Systems (Curran Associates Inc. 2021) vol. 34 p.\u00a0251."},{"key":"e_1_3_2_108_2","unstructured":"MetaAI Fairo: A modular embodied agent architecture and platform for building embodied agents (2021); https:\/\/github.com\/facebookresearch\/fairo."},{"key":"e_1_3_2_109_2","doi-asserted-by":"crossref","unstructured":"S. Kohlbrecher J.\u00a0Meyer O.\u00a0von Stryk U.\u00a0Klingauf \u201cA flexible and scalable SLAM system with full 3D motion estimation \u201d paper presented at 2011 IEEE International Symposium on Safety Security and Rescue Robotics Kyoto Japan 1 to 5 November 2011.","DOI":"10.1109\/SSRR.2011.6106777"},{"key":"e_1_3_2_110_2","doi-asserted-by":"crossref","unstructured":"K. He G.\u00a0Gkioxari P.\u00a0Doll\u00e1r R.\u00a0Girshick \u201cMask R-CNN \u201d in Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV 2017) Venice Italy 22 to 29 October 2017 (IEEE 2017) pp.\u00a02980\u20132988.","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_111_2","doi-asserted-by":"crossref","unstructured":"K. He X.\u00a0Zhang S.\u00a0Ren J.\u00a0Sun \u201cDeep residual learning for image recognition \u201d in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas NV 27 to 30 June 2016 (IEEE 2016) pp.\u00a0770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_112_2","doi-asserted-by":"crossref","unstructured":"F. Xia A.\u00a0R.\u00a0Zamir Z.\u00a0He A.\u00a0Sax J.\u00a0Malik S.\u00a0Savarese \u201cGibson Env: Real-world perception for embodied agents \u201d in Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Salt Lake City UT 18 to 23 June 2018 (IEEE 2018) pp.\u00a09068\u20139079.","DOI":"10.1109\/CVPR.2018.00945"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.93.4.1591"},{"key":"e_1_3_2_114_2","unstructured":"J. Chung C.\u00a0Gulcehre K.\u00a0Cho Y.\u00a0Bengio Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 [cs.NE] (11 December 2014)."},{"key":"e_1_3_2_115_2","unstructured":"J. Jiang L.\u00a0Zheng F.\u00a0Luo Z.\u00a0Zhang Rednet: Residual encoder-decoder network for indoor rgb-d semantic segmentation. arXiv:1806.01054 [cs.CV] (4 June 2018)."},{"key":"e_1_3_2_116_2","doi-asserted-by":"crossref","unstructured":"S. Song S.\u00a0P.\u00a0Lichtenberg J.\u00a0Xiao \u201cSun RGB-D: A RGB-D scene understanding benchmark suite \u201d in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston MA 7 to 12 June 2015 (IEEE 2015) pp.\u00a0567\u2013576.","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"e_1_3_2_117_2","doi-asserted-by":"crossref","unstructured":"S. Choi Q.-Y. Zhou V.\u00a0Koltun \u201cRobust reconstruction of indoor scenes \u201d in Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston MA 7 to 12 June 2015 (IEEE 2015) pp.\u00a05556\u20135565.","DOI":"10.1109\/CVPR.2015.7299195"},{"key":"e_1_3_2_118_2","unstructured":"A. Murali T.\u00a0Chen K.\u00a0V.\u00a0Alwala D.\u00a0Gandhi L.\u00a0Pinto S.\u00a0Gupta A.\u00a0Gupta PyRobot: An open-source robotics framework for research and benchmarking. arXiv:1906.08236 [cs.RO] (19 June 2019)."}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.adf6991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T07:24:12Z","timestamp":1729668252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.adf6991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":117,"journal-issue":{"issue":"79","published-print":{"date-parts":[[2023,6,28]]}},"alternative-id":["10.1126\/scirobotics.adf6991"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.adf6991","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"2022-11-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eadf6991"}}