{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:30:16Z","timestamp":1781886616575,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Project of the New Generation of Artificial Intelligence","award":["2018AAA0102900"],"award-info":[{"award-number":["2018AAA0102900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.<\/jats:p>","DOI":"10.3390\/e25040657","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T05:29:29Z","timestamp":1681450169000},"page":"657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Semantic Representation of Robot Manipulation with Knowledge Graph"],"prefix":"10.3390","volume":"25","author":[{"given":"Runqing","family":"Miao","sequence":"first","affiliation":[{"name":"School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingxuan","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiming","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengyi","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kaelbling, L.P., and Lozano-P\u00e9rez, T. 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