Artificial Life and Robotics

, Volume 19, Issue 2, pp 193–200 | Cite as

Estimation of physical interaction between a musculoskeletal robot and its surroundings

  • Kenji Urai
  • Yuya Okadome
  • Yoshihiro Nakata
  • Yutaka Nakamura
  • Hiroshi Ishiguro
Original Article

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.

Keywords

Musculoskeletal robot Gaussian process regression Locality-sensitive hashing 

References

  1. 1.
    Shiomi M, Kanda T, Koizumi S, Ishiguro H, Hagita N (2008) Group attention control for communication robots. Int J Humanoid Robot 5(4):587–608CrossRefGoogle Scholar
  2. 2.
    Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press, MassachusettsGoogle Scholar
  3. 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 10:857–882MathSciNetMATHGoogle Scholar
  4. 4.
    Hosoda K, Sakaguchi Y, Takayama H, Takuma T (2010) Pneumatic-driven jumping robot with anthropomorphic muscular skeleton structure. Auton Robots 28(3):307–316CrossRefGoogle Scholar
  5. 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):187–202CrossRefGoogle Scholar
  6. 6.
    Ghahramani Z, Hinton GE (1998) Variational learning for switching state-space models. Neural Comput 12:963–996Google Scholar
  7. 7.
    Watkins CJCH, Dayan P (1992) Technical note: Qlearning. Mach Learn 8:279–292. doi:10.1007/BF00992698 MATHGoogle Scholar
  8. 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 2397–2403Google Scholar
  9. 9.
    Peters J, Schaal S (2008) Reinforcement learning of motor skills with policy gradients. Neural Netw 21(4):682–697CrossRefGoogle Scholar
  10. 10.
    Bishop CM (2006) Pattern recognition and machine learning, 1st edn, 2nd printing edition. Springer, Berlin, October 2006.Google Scholar
  11. 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 609–616Google Scholar
  12. 12.
    Smola AJ, Bartlett P (2001) Sparse greedy gaussian process regression. In: Advances in neural information processing systems, vol 13. MIT Press, Massachusetts, pp 619–625Google Scholar
  13. 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 604–613Google Scholar
  14. 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 253–262Google Scholar
  15. 15.
    Lieber RL (2002) Skeletal muscle structure, function, and plasticity: the physiological basis of rehabilitation, 2nd edn. Williams & Wilkins, PhiladelphiaGoogle Scholar
  16. 16.
    Hogan N (1984) Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans Autom Control 29(8):681–690CrossRefMATHGoogle Scholar
  17. 17.
    Wisse M, van der Linde RQ (2007) Delft pneumatic bipeds. In: Springer tracts in advanced robotics, vol 34. Springer, BerlinGoogle Scholar
  18. 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, BerlinGoogle Scholar
  19. 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):315321Google Scholar
  20. 20.
    Niiyama R, Kuniyoshi Y (2010) Design principle based on maximum output force profile for a musculoskeletal robot. Ind Robot Int J 37(3):250–255CrossRefGoogle Scholar
  21. 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):166–170CrossRefGoogle Scholar
  22. 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 3153–3160Google Scholar
  23. 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 17–25Google Scholar
  24. 24.
    Waterhouse SR (1997) Classification and regression using mixtures of expertsGoogle Scholar
  25. 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 approachesGoogle Scholar

Copyright information

© ISAROB 2014

Authors and Affiliations

  • Kenji Urai
    • 1
  • Yuya Okadome
    • 1
  • Yoshihiro Nakata
    • 1
  • Yutaka Nakamura
    • 1
  • Hiroshi Ishiguro
    • 1
  1. 1.Osaka UniversityOsakaJapan

Personalised recommendations