Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition
<|reference_start|>Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition: The flexible under-actuated musculoskeletal hand is superior in its adaptability and impact resistance. On the other hand, since the relationship between sensors and actuators cannot be uniquely determined, almost all its controls are based on feedforward controls. When grasping and using a tool, the contact state of the hand gradually changes due to the inertia of the tool or impact of action, and the initial contact state is hardly kept. In this study, we propose a system that trains the predictive network of sensor state transition using the actual robot sensor information, and keeps the initial contact state by a feedback control using the network. We conduct experiments of hammer hitting, vacuuming, and brooming, and verify the effectiveness of this study.<|reference_end|>
@article{kawaharazuka2024stable,
    title={Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the
  Predictive Model of Sensor State Transition},
    author={Kento Kawaharazuka and Kei Tsuzuki and Moritaka Onitsuka and Yuki
  Asano and Kei Okada and Koji Kawasaki and Masayuki Inaba},
    journal={arXiv preprint arXiv:2406.17136},
    year={2024},
    doi={10.1109/ICRA40945.2020.9197188},
    archivePrefix={arXiv},
    eprint={2406.17136},
    primaryClass={cs.RO}
}