Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures
<|reference_start|>Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures: 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.<|reference_end|>
@article{kawaharazuka2024long-time,
    title={Long-time Self-body Image Acquisition and its Application to the Control
  of Musculoskeletal Structures},
    author={Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka, Yuki
  Asano, Kei Okada, Koji Kawasaki, Masayuki Inaba},
    journal={arXiv preprint arXiv:2404.05293},
    year={2024},
    doi={10.1109/LRA.2019.2923968},
    archivePrefix={arXiv},
    eprint={2404.05293},
    primaryClass={cs.RO}
}