Imitation Learning of Human Operation Based on Visual Demonstration
Pages 71 - 76
Abstract
In this paper, we propose an imitation learning method based on the optimization of the dynamic movement primitives (DMPs). The DMPs framework is one of the common methods of imitation learning because of its adaptability in time and space. On the basis of the traditional framework, we add the consideration of the dynamic performance of the robot during the reproduction process. We optimize the learned parameters (via DMPs) to reduce the average torque of robot joints while keeping the trajectory error small. The proposed method is evaluated with an experiment where a 6-degrees of freedom (6-DOF) robot learned a pick-place task from the visual demonstration of a human teacher. The experimental results show that our method reduced the average torque of robot joints by 73.4 N.m, which proves the effectiveness of the proposed method.
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Published In
August 2020
114 pages
ISBN:9781450388023
DOI:10.1145/3425577
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 January 2021
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- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China
- National Key Research and Development Project of China
Conference
ICCCV'20
ICCCV'20: 2020 the 3rd International Conference on Control and Computer Vision
August 23 - 25, 2020
Macau, China
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