Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917866184376320 |
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| author | Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Shinjo, Koki Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki |
| author_facet | Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Shinjo, Koki Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki |
| contents | The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving Kawaharazuka, Kento Tsuzuki, Kei Makino, Shogo Onitsuka, Moritaka Shinjo, Koki Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki Robotics The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study. |
| title | Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.08270 |