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Estimation of physical interaction between a musculoskeletal robot and its surroundings

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Abstract

Recently, robots are expected to support our daily lives in real environments. In such environments, however, there are a lot of obstacles and the motion of the robot is affected by them. In this research, we develop a musculoskeletal robotic arm and a system identification method for coping with external forces while learning the dynamics of complicated situations, based on Gaussian process regression (GPR). The musculoskeletal robot has the ability to cope with external forces by utilizing a bio-inspired mechanism. GPR is an easy-to-implement method, but can handle complicated prediction tasks. The experimental results show that the behavior of the robot while interacting with its surroundings can be predicted by our method.

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Acknowledgments

This work was supported in part by JSPS KAKENHI Grant-in-Aid for Young Scientists (A) No.80720664.

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Authors and Affiliations

  1. Osaka University, Osaka, Japan

    Kenji Urai, Yuya Okadome, Yoshihiro Nakata, Yutaka Nakamura & Hiroshi Ishiguro

Authors
  1. Kenji Urai
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  2. Yuya Okadome
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  3. Yoshihiro Nakata
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  4. Yutaka Nakamura
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  5. Hiroshi Ishiguro
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Corresponding author

Correspondence to Yutaka Nakamura.

Additional information

This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014.

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Urai, K., Okadome, Y., Nakata, Y. et al. Estimation of physical interaction between a musculoskeletal robot and its surroundings. Artif Life Robotics 19, 193–200 (2014). https://doi.org/10.1007/s10015-014-0148-y

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  • DOI: https://doi.org/10.1007/s10015-014-0148-y

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