Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929305546653696 |
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| author | Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki |
| author_facet | Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki |
| contents | The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 hours, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki Robotics The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 hours, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively. |
| title | Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.05293 |