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Article type: Research Article
Authors: Jin, Yongbinga | Ran, Tenga; * | Yuan, Lianga; b; c; * | Lv, Kaia | Wang, Guolianga; d | Xiao, Wendonga
Affiliations: [a] The School of Mechanical Engineering, Xinjiang University, Urumqi, China | [b] The Beijing Advanced Innovation Center for Soft Matter Sciencey, Beijing University of Chemical Technology, Beijing, China | [c] The Engineering and the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China | [d] Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
Correspondence: [*] Corresponding author. Teng Ran, E-mail: ranteng@xju.edu.cn and Liang Yuan, Email: yuanliang@mail.buct.edu.cn.
Abstract: Handwriting robots as an application of Imitation Learning (IL). However, most methods have poor accuracy of trajectory generation under task constraints, and models are less robust to changes in demonstration data. This paper proposes an IL algorithm named Bagging in Hidden Semi-Markov Model (BHSMM). The demonstration data is first divided into several sub-datasets, and each sub-dataset is encoded into several basic learning models by Hidden Semi-Markov Models (HSMM). Then the relationship between the task constraint points and the basic learning models is used to derive the weights. Finally, the trajectories adapted to the task constraints are generated based on the weights. We conducted experiments on the handwritten dataset LASA and compared the accuracy error with the original HSMM method. The results show that the BHSMM can generate trajectories that satisfy the position and velocity constraints and is more robust to changes in the demonstration data than the HSMM. In addition, satisfactory results are obtained in trajectory generation for real robot handwriting.
Keywords: Imitation learning, human-robot collaboration, handwriting robot, BHSMM
DOI: 10.3233/JIFS-237275
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6325-6335, 2024
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