Abstract
Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design.
This work was supported by the Major Project of the New Generation of Artificial Intelli-gence (No. 2018AAA0102900), the National Natural Science Foundation of China (No. 62173233), the Shenzhen Science and Technology Innovation Commission project (JCYJ20210324094401005, JCYJ20220531102809022), the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011582).
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Miao, S. et al. (2023). Hierarchical Knowledge Representation of Complex Tasks Based on Dynamic Motion Primitives. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_31
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DOI: https://doi.org/10.1007/978-981-99-0617-8_31
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