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Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation

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Abstract

The proposed method can generate an optimal feedforward control input and the corresponding optimal walking trajectory minimizing the \(L_2\) norm of the control input by iteration of laboratory experiments. Since a general walking motion involves discontinuous velocity transitions caused by the collision with the ground, the proposed method consists of the combination of a trajectory learning part and an estimation part of the discontinuous state transition mapping using the stored experimental data. We apply the proposed method to a kneed biped robot with a torso, where we also provide a technique to generate an optimal gait not only being energy-efficient but also avoiding the foot-scuffing problem.

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Acknowledgements

This work was partially supported by JSPS KAKENHI Grant Numbers JP17H03282 and JP18K04202.

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Authors and Affiliations

  1. Osaka University, 2-1, Yamadaoka, Suita, Osaka, 565-0871, Japan

    Satoshi Satoh

  2. Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

    Kenji Fujimoto

Authors
  1. Satoshi Satoh
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  2. Kenji Fujimoto
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Corresponding author

Correspondence to Satoshi Satoh.

Additional information

This work was presented in part at the 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Kyoto, October 29–November 1, 2017.

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Satoh, S., Fujimoto, K. Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation. Artif Life Robotics 23, 489–497 (2018). https://doi.org/10.1007/s10015-018-0476-4

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  • DOI: https://doi.org/10.1007/s10015-018-0476-4

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