Skip to main content
SpringerLink
Log in

Estimation of physical interaction between a musculoskeletal robot and its surroundings

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Shiomi M, Kanda T, Koizumi S, Ishiguro H, Hagita N (2008) Group attention control for communication robots. Int J Humanoid Robot 5(4):587–608

    Article  Google Scholar 

  2. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press, Massachusetts

  3. Foster L, Waagen A, Aijaz N, Hurley M, Luis A, Rinsky J, Satyavolu C, Way MJ, Gazis P, Srivastava AN (2009) Stable and efficient gaussian process calculations. J Mach Learn Res 10:857–882

    MathSciNet  MATH  Google Scholar 

  4. Hosoda K, Sakaguchi Y, Takayama H, Takuma T (2010) Pneumatic-driven jumping robot with anthropomorphic muscular skeleton structure. Auton Robots 28(3):307–316

    Article  Google Scholar 

  5. Fukuoka Y, Kimura H, Cohen AH (2003) Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts. Int J Robot Res 22(3–4):187–202

    Article  Google Scholar 

  6. Ghahramani Z, Hinton GE (1998) Variational learning for switching state-space models. Neural Comput 12:963–996

    Google Scholar 

  7. Watkins CJCH, Dayan P (1992) Technical note: Qlearning. Mach Learn 8:279–292. doi:10.1007/BF00992698

    MATH  Google Scholar 

  8. Theodorou E, Buchli J, Schaal S (2010) Reinforcement learning of motor skills in high dimensions: a path integral approach. In: 2010 IEEE international conference on robotics and automation (ICRA), pp 2397–2403

  9. Peters J, Schaal S (2008) Reinforcement learning of motor skills with policy gradients. Neural Netw 21(4):682–697

    Article  Google Scholar 

  10. Bishop CM (2006) Pattern recognition and machine learning, 1st edn, 2nd printing edition. Springer, Berlin, October 2006.

  11. Lawrence N, Seeger M, Herbrich R (2003) Fast sparse gaussian process methods: the informative vector machine. In: Thrun S, Becker S, Obermayer K (eds) Advances in neural information processing systems, vol 15. MIT Press, Cambridge, pp 609–616

  12. Smola AJ, Bartlett P (2001) Sparse greedy gaussian process regression. In: Advances in neural information processing systems, vol 13. MIT Press, Massachusetts, pp 619–625

  13. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing, STOC98. ACM, New York, pp 604–613

  14. Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the twentieth annual symposium on computational geometry, SCG04. ACM, New York, pp 253–262

  15. Lieber RL (2002) Skeletal muscle structure, function, and plasticity: the physiological basis of rehabilitation, 2nd edn. Williams & Wilkins, Philadelphia

  16. Hogan N (1984) Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans Autom Control 29(8):681–690

    Article  MATH  Google Scholar 

  17. Wisse M, van der Linde RQ (2007) Delft pneumatic bipeds. In: Springer tracts in advanced robotics, vol 34. Springer, Berlin

  18. Vanderborght B (2010) Dynamic stabilisation of the biped lucy powered by actuators with controllable stiffness. In: Springer tracts in advanced robotics, vol 63. Springer, Berlin

  19. Sugahara A, Nakamura Y, Fukuyori I, Matsumoto Y, Ishiguro H (2010) Generating circular motion of a human-like robotic arm using attractor selection model. J Robot Mechatron 22(3):315321

    Google Scholar 

  20. Niiyama R, Kuniyoshi Y (2010) Design principle based on maximum output force profile for a musculoskeletal robot. Ind Robot Int J 37(3):250–255

    Article  Google Scholar 

  21. Nakata Y, Ishiguro H, Hirata K (2011) Dynamic analysis method for electromagnetic artificial muscle actuator under pid control. IEEJ Trans Ind Appl 131(2):166–170

    Article  Google Scholar 

  22. Nakata Y, Ide A, Nakamura Y, Hirata K, Ishiguro H (2012) Hopping of a monopedal robot with a biarticular muscle driven by electromagnetic linear actuators. In: Proceedings of the IEEE international conference on robotics and automation, vol 2012, pp 3153–3160

  23. Okadome Y, Nakamura Y, Shikauchi Y, Ishii S, Ishiguro H (2013) Fast approximation method for Gaussian process regression using hash function for non-uniformly distributed data. In: International conference on artificial neural networks (ICANN), September 2013, pp 17–25

  24. Waterhouse SR (1997) Classification and regression using mixtures of experts

  25. Thrun S (1992) The role of exploration in learning control. In: White DA, Sofge DA (eds) Handbook for intelligent control: neural. van nostrand reinhold, fuzzy and adaptive approaches

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Osaka University, Osaka, Japan

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

Authors
  1. Kenji Urai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuya Okadome
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshihiro Nakata
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yutaka Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hiroshi Ishiguro
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation