A knowledge-based task planning approach for robot multi-task manipulation
Autor: | Deshuai Zheng, Jin Yan, Tao Xue, Yong Liu |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | Complex & Intelligent Systems, Vol 10, Iss 1, Pp 193-206 (2023) |
Druh dokumentu: | article |
ISSN: | 2199-4536 2198-6053 |
DOI: | 10.1007/s40747-023-01155-8 |
Popis: | Abstract Task planning is a crucial component in facilitating robot multi-task manipulations. Language-based task planning methods offer practicality in receiving commands from humans in real-life scenarios and require only low-cost labeled data. However, existing methods often rely on sequence models for planning, which primarily focus on mapping language to sequences of sub-tasks while neglecting the knowledge about tasks and objects. To overcome these limitations, we propose a knowledge-based task planning approach called Recurrent Graph Convolutional Network (RGCN). It is devised with a novel structure that combined GCN (Kipf and Welling in International Conference on Learning Representations (ICLR), 2017) and LSTM (Hochreiter and chmidhuber in Neural Comput 9 (8): 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735 ) which enables it to leverage knowledge graph data and historical predictions. The experimental results demonstrate that our approach achieves the impressive task planning success rate of $${95.7\%}$$ 95.7 % , surpassing the best baseline method significantly, which achieves $${78.7\%}$$ 78.7 % . Furthermore, we evaluate the performance of multi-task manipulation across a specific set of 20 tasks within a simulated environment. Notably, RGCN combined with pre-trained primitive tasks exhibits the highest success rate compared with state-of-art multi-task learning methods. Our method is proven to be significant for language-conditioned task planning and is qualified for instructing robots for multi-task manipulation. |
Databáze: | Directory of Open Access Journals |
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