| Robot Modeling and Contro |
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| Ontology-based humanoid cognition and reasoning of object attributes |
| LI Ci-ci, TIAN Guo-hui, ZHANG Meng-yang, ZHANG Ying |
| School of Control Science and Engineering, Shandong University, Jinan 250061, China |
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Abstract A method to construct a semantic knowledge base based on object attributes was proposed in order to help robots have humanoid cognition and reasoning about object knowledge to assist them to operate objects. Visual attributes, category attributes, physical attributes, affordance, operation attributes were used to describe object knowledge in a systematic and orderly way, and web ontology language was used to model the object knowledge. The rules between attributes were made by semantic web rule language to fulfil the cognition and reasoning about object attributes. A simulation platform was used to verify that the knowledge base can guide the operation of objects. The query results to the knowledge base show that robots can obtain different kinds of semantic information in a unified form.
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Received: 24 January 2018
Published: 26 June 2018
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基于本体的物品属性类人认知及推理
为了实现服务机器人对物品知识的类人认知和推理，以辅助机器人执行物品操作任务，提出基于属性的家庭物品语义知识库构建方法.通过对物品知识进行分类，使用视觉、类别、物理、功能、操作属性对物品知识进行系统化和条理化的描述，结合本体建模语言对物品知识进行建模；利用语义网络规则语言，制定针对物品属性的推理规则（包含视觉、类别、物理-功能、视觉-类别、类别-类别），实现对物品属性的自主认知及推理.在实验室搭建的机器人物品操作仿真平台上验证了物品语义知识库对机器人操作物品的指导作用.对语义知识库的查询结果表明，机器人可以以统一的形式获取与物品相关的多种高层语义信息.
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| [1] 戈尔茨坦.认知心理学-心智、研究与你的生活[M].3版.张明,王佳莹,康静梅,等,译.北京:中国轻工业出版社,2015:315-333.
[2] FARHADI A, ENDRES I, HOIEM D, et al. Describing objects by their attributes[C]//Proceedings of CVPR. Miami:IEEE, 2009:1778-1785.
[3] SUN Y Y, BO L, FOX D. Attribute based object identification[C]//Proceedings of ICRA. Karlsruhe:IEEE, 2013:2096-2103.
[4] ZHU Y, FATHI A, LI F F. Reasoning about object affordances in a knowledge base representation[C]//Proceedings of ECCV. Zurich:Springer, 2014:408-424.
[5] LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//Proceedings of CVPR. Miami:IEEE, 2009:951-958.
[6] DUAN D, PARIKH D, CRANDALL D, et al. Discovering localized attributes for fine-grained recognition[C]//Proceedings of CVPR 2012. Providence: IEEE, 2012:3474-3481.
[7] GUPATA A, KEMBHAVI A, DAVIS L S. Observing human-object interactions:using spatial and functional compatibility for recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31(10):1775-1789.
[8] RUIKEN D, WONG J M, LIU T Q, et al. Affordance-based active belief:recognition using visual and manual actions[C]//Proceedings of IROS. Daejeon:IEEE, 2016:5312-5317.
[9] KOPPULA H S, GUPTA R, SAXENA A. Learning human activities and object affordances from RGB-D videos[J]. International Journal of Robotics Research, 2013, 32(8):951-970.
[10] KOPPULA H S, GUPTA R, SAXENA A. Anticipating human activities using object affordances for reactive robotic response[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(1):14-29.
[11] LINDNER F, ESCHENBACH C. Affordance-based activity placement in human-robot shared environments[C]//Proceedings of ICSR. Bristol:Spring, 2013:94-103.
[12] TENORTH M, BEETZ M. Representations for robot knowledge in the KnowRob framework[J]. Artificial Intelligence, 2015, 247(2017):151-169.
[13] BASILE V, CABRIO E, GANDON F. Building a general knowledge base of physical objects for robots[C]//Proceedings of ESWC. Heraklion:Springer, 2016:8-11.
[14] 阎红灿.本体建模与语义Web知识发现[M].北京:清华大学出版社,2015:145-146.
[15] 安东尼乌.语义网基础教程[M].陈小平,周熠,杨斌,等,译.北京:机械工业出版社,2014:87-89.
[16] SILBERER C, FERRARI V, LAPATA M. Models of semantic representation with visual attributes[C]//2013 ACL Annual Meeting of the Association for Computational Linguistics. Sofia:[s. n.], 2013:572-582.
[17] FELLBAUM C, MILLER G. WordNet:an electronic Lexical database[M]. London:MIT, 1998:71-423.
[18] TENORTH M, PROFANTER S, BALINT-BENCZEDI F, et al. Decomposing CAD models of objects of daily use and reasoning about their functional parts[C]//Proceedings of IROS. Vilamoura:IEEE, 2013:5943-5949.
[19] HERMANS T, REHG J. M, BOBICK A. Affordance prediction via learned object attributes[C]//Proceedings of the ICRA. Portugal:[s. n.], 2011:1-8.
[20] 蒋湛,姚晓明,林兰芬.基于特征自适应的本体映射方法[J].浙江大学学报:工学版,2014,48(1):76-84. JIANG Zhan, YAO Xiao-ming, LIN Lan-fen. Feature-based adaptive method of ontology mapping[J]. Journal of Zhejiang University:Engineering Science, 2014, 48(1):76-84.
[21] HORROCKS I, PATEL-SCHNEIDER P F, BOLEY H, et al. SWRL:a semantic Web rule language combining OWL and RuleML[EB/OL]. (2004-03-21)[2018-01-24]. https://www.w3.org/Submission/SWRL/.
[22] UEDA T, IZUMI N, MORITA Y, et al. Jena-Java framework for developing semantic Web applications[J]. Journal of Japanese Society for Artificial Intelligence, 2004, 19(3):325-333.
[23] MYSQL A. MySQL:the world's most popular open source database[EB/OL].[2018-01-24]. https://www.mysql.com/. |
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