A syntactic method for robot imitation learning of complex sequence task
Robotic Intelligence and Automation
ISSN: 2754-6969
Article publication date: 2 May 2023
Issue publication date: 23 May 2023
Abstract
Purpose
Aiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.
Design/methodology/approach
The paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.
Findings
It is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.
Practical implications
Using this strategy, the robot completes the fourth-order Hanoi tower task has been verified.
Originality/value
An imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.
Keywords
Acknowledgements
Funding: Joint Open Fund Program of Liaoning Science and Technology Plan, 2022-KF-12-06.
Citation
Du, Y., Jian, J., Zhu, Z., Pan, D., Liu, D. and Tian, X. (2023), "A syntactic method for robot imitation learning of complex sequence task", Robotic Intelligence and Automation, Vol. 43 No. 2, pp. 132-143. https://doi.org/10.1108/RIA-05-2022-0127
Publisher
:Emerald Publishing Limited
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