{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T19:59:04Z","timestamp":1764014344830,"version":"3.45.0"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022ZD0119602"],"award-info":[{"award-number":["2022ZD0119602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/lra.2025.3622906","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T17:42:36Z","timestamp":1760722956000},"page":"12357-12364","source":"Crossref","is-referenced-by-count":0,"title":["Learning to Explore Efficiently: Heterogeneous Topological Graphs and Lightweight Global Reasoning for Robotic Exploration"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0231-1821","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"first","affiliation":[{"name":"School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5396-6767","authenticated-orcid":false,"given":"Kairao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9371-0216","authenticated-orcid":false,"given":"Yiqing","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4805-9269","authenticated-orcid":false,"given":"Junlong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5670-2249","authenticated-orcid":false,"given":"Xiaoxun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0622-4510","authenticated-orcid":false,"given":"Jinze","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2579-7004","authenticated-orcid":false,"given":"Hui","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10611179"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3511375"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/280765.280773"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2021.xvii.018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3236573"},{"key":"ref6","first-page":"2193","article-title":"Learning to explore using active neural SLAM","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chaplot","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3379804"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112124"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/2.30720"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202315"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981136"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3334103"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3051563"},{"key":"ref14","first-page":"2713","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2016"},{"key":"ref15","first-page":"2920","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velikovi","year":"2018"},{"key":"ref16","first-page":"11983","article-title":"Graph transformer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yun","year":"2019"},{"key":"ref17","first-page":"14048","article-title":"On the bottleneck of graph neural networks and its practical implications","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Alon","year":"2021"},{"key":"ref18","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Katharopoulos","year":"2020"},{"key":"ref19","first-page":"64753","article-title":"SGFormer: Simplifying and empowering transformers for large-graph representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3282783"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1998.712192"},{"article-title":"Go-explore: A new approach for hard-exploration problems","year":"2019","author":"Ecoffet","key":"ref22"},{"key":"ref23","first-page":"22491","article-title":"Learning coverage paths in unknown environments with deep reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jonnarth","year":"2024"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.70"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abc5986"},{"article-title":"Reward hacking in reinforcement learning","year":"2024","author":"Weng","key":"ref26"},{"issue":"181","key":"ref27","first-page":"1","article-title":"Curriculum learning for reinforcement learning domains: A framework and survey","volume":"21","author":"Narvekar","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref28","article-title":"dD triangulations","volume-title":"CGAL User and Reference Manual","author":"Devillers","year":"2024"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"article-title":"Proximal policy optimization algorithms","year":"2017","author":"Schulman","key":"ref30"},{"issue":"274","key":"ref31","first-page":"1","article-title":"CleanRL: High-quality single-file implementations of deep reinforcement learning algorithms","volume":"23","author":"Huang","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3264163"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7083369\/11215960\/11206341.pdf?arnumber=11206341","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T19:02:41Z","timestamp":1764010961000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11206341\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":32,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/lra.2025.3622906","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"type":"electronic","value":"2377-3766"},{"type":"electronic","value":"2377-3774"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}