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Appl."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    The ability to associate sight with touch is essential for human and robot agents to understand material properties and to interact with the physical world. In the real-world scenarios, the robot agents often operate in a dynamically changing environment where new classes of objects are continually collected by visual and tactile sensors. In this article, we define this scenario as\n                    <jats:italic toggle=\"yes\">V<\/jats:italic>\n                    isuo-\n                    <jats:italic toggle=\"yes\">T<\/jats:italic>\n                    actile\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    lass-\n                    <jats:italic toggle=\"yes\">I<\/jats:italic>\n                    ncremental\n                    <jats:italic toggle=\"yes\">L<\/jats:italic>\n                    earning (VT-CIL). In practical VT-CIL, the robot needs to adapt to a new environment with constrained storage and computing resources, and suffers from the severe forgetting of vision and touch knowledge about old environments. To alleviate this problem, we consider visuo-tactile correlations in VT-CIL and propose a novel framework. It efficiently incorporates the Visuo-Tactile Cross-Modal Pseudo-Label-Consistent (VT-CMPLC) constraint, Dual-Visuo-Tactile Exemplars (DVT-E), and the Dual-Visuo-Tactile-Compatible (DVT-C) constraint. The old visual\u2013tactile classes are preserved by the VT-CMPLC constraint and DVT-E, while the visuo-tactile correlations and the VT-CMPLC and DVT-E capabilities are enhanced by the DVT-C constraint. We built two benchmarks, the Touch-and-Go Class-Incremental (TaG-CI) benchmark and the ObjectFolder-Real Class-Incremental (OFR-CI) benchmark. Experimental results on TaG-CI and OFR-CI benchmarks demonstrate the effectiveness of our method against previous state-of-the-art class-incremental learning methods in VT-CIL.\n                  <\/jats:p>","DOI":"10.1145\/3754452","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T15:12:49Z","timestamp":1753456369000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Visuo-Tactile Class-Incremental Learning"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5149-530X","authenticated-orcid":false,"given":"Hao","family":"Fu","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1094-8204","authenticated-orcid":false,"given":"Fengyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Yale University, New Haven, Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7576-8282","authenticated-orcid":false,"given":"Boyang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8106-9768","authenticated-orcid":false,"given":"Wei","family":"Ji","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8906-4534","authenticated-orcid":false,"given":"Hanbin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7174-8663","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7410-2590","authenticated-orcid":false,"given":"Roger","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0293-2656","authenticated-orcid":false,"given":"Hui","family":"Qian","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00088"},{"key":"e_1_3_1_3_2","first-page":"1093","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Asadi Nader","year":"2023","unstructured":"Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, and Eugene Belilovsky. 2023. 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