Explainable Hierarchical Imitation Learning for Robotic Drink Pouring

Zhang D, Li Q, Zheng Y, Wei L, Zhang D, Zhang Z (2021)
IEEE Transactions on Automation Science and Engineering.

Zeitschriftenaufsatz | E-Veröff. vor dem Druck | Englisch
 
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Autor*in
Zhang, Dandan; Li, QiangUniBi ; Zheng, Yu; Wei, Lei; Zhang, Dongsheng; Zhang, Zhengyou
Abstract / Bemerkung
To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreover, with the EHIL method, a logical graph can be constructed for task execution, through which the decision-making process for action generation can be made explainable to users and the causes of failure can be traced out. Based on the logical graph, the framework is manipulable to achieve different targets while the adaptability to unseen scenarios can be achieved in an explainable manner. A series of experiments have been conducted to verify the effectiveness of the proposed method. Results indicate that EHIL outperforms the traditional behavior cloning method in terms of success rate, adaptability, manipulability, and explainability.
Stichworte
Robots; Task analysis; Containers; Service robots; Adaptation models; Data models; Liquids; Robotic pouring; imitation learning; model; learning; service robots
Erscheinungsjahr
2021
Zeitschriftentitel
IEEE Transactions on Automation Science and Engineering
ISSN
1545-5955
eISSN
1558-3783
Page URI
https://pub.uni-bielefeld.de/record/2960721

Zitieren

Zhang D, Li Q, Zheng Y, Wei L, Zhang D, Zhang Z. Explainable Hierarchical Imitation Learning for Robotic Drink Pouring. IEEE Transactions on Automation Science and Engineering. 2021.
Zhang, D., Li, Q., Zheng, Y., Wei, L., Zhang, D., & Zhang, Z. (2021). Explainable Hierarchical Imitation Learning for Robotic Drink Pouring. IEEE Transactions on Automation Science and Engineering. https://doi.org/10.1109/TASE.2021.3138280
Zhang, D., Li, Q., Zheng, Y., Wei, L., Zhang, D., and Zhang, Z. (2021). Explainable Hierarchical Imitation Learning for Robotic Drink Pouring. IEEE Transactions on Automation Science and Engineering.
Zhang, D., et al., 2021. Explainable Hierarchical Imitation Learning for Robotic Drink Pouring. IEEE Transactions on Automation Science and Engineering.
D. Zhang, et al., “Explainable Hierarchical Imitation Learning for Robotic Drink Pouring”, IEEE Transactions on Automation Science and Engineering, 2021.
Zhang, D., Li, Q., Zheng, Y., Wei, L., Zhang, D., Zhang, Z.: Explainable Hierarchical Imitation Learning for Robotic Drink Pouring. IEEE Transactions on Automation Science and Engineering. (2021).
Zhang, Dandan, Li, Qiang, Zheng, Yu, Wei, Lei, Zhang, Dongsheng, and Zhang, Zhengyou. “Explainable Hierarchical Imitation Learning for Robotic Drink Pouring”. IEEE Transactions on Automation Science and Engineering (2021).

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