Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910443653562368 |
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| author | Zhang, Yu Zou, Yongxiang Zhang, Haoyu Xia, Xiuze Cheng, Long |
| author_facet | Zhang, Yu Zou, Yongxiang Zhang, Haoyu Xia, Xiuze Cheng, Long |
| contents | Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided data. The model presented in this paper employs a straightforward neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08849 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks Zhang, Yu Zou, Yongxiang Zhang, Haoyu Xia, Xiuze Cheng, Long Robotics Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided data. The model presented in this paper employs a straightforward neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment. |
| title | Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.08849 |