Journal of Bionic Engineering

, Volume 15, Issue 2, pp 329–340 | Cite as

Learning Control of Quadruped Robot Galloping

  • Qingyu Liu
  • Xuedong Chen
  • Bin Han
  • Zhiwei Luo
  • Xin Luo


Achieving galloping gait in quadruped robots is challenging, because the galloping gait exhibits complex dynamical behaviors of a hybrid nonlinear under-actuated dynamic system. This paper presents a learning approach to quadruped robot galloping control. The control function is obtained through directly approximating real gait data by learning algorithm, without consideration of robot’s model and environment where the robot is located. Three motion control parameters are chosen to determine the galloping process, and the deduced control function is learned iteratively with modified Locally Weighted Projection Regression (LWPR) algorithm. Experiments conducted upon the bioinspired quadruped robot, AgiDog, indicate that the robot can improve running performance continuously along the learning process, and adapt itself to model and environment uncertainties.


quadruped gallop dynamic running LWPR learning bioinspiration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work is partially supported by the National Natural Science Foundation of China (NSFC) under grant numbers 61175097 and 51475177, and the Research Fund for the Doctoral Programme of Higher Education of China (RFDP) under grant number 20130142110081.


  1. [1]
    Raibert M H. Legged Robots That Balance, The MIT Press, Massachusetts, USA, 1986.zbMATHGoogle Scholar
  2. [2]
    Muybridge E. Horses and Other Animals in Motion: 45 Classic Photographic Sequences, Dover Publications, New York, USA, 1985.Google Scholar
  3. [3]
    Chen D, Li N, Wang H, Chen L. Effect of flexible spine motion on energy efficiency in quadruped running. Journal of Bionic Engineering, 2017, 14, 716–725.CrossRefGoogle Scholar
  4. [4]
    Nie H, Sun R, Hu L, Su Z, Hu W. Control of a cheetah robot in passive bounding gait. Journal of Bionic Engineering, 2016, 13, 283–291.CrossRefGoogle Scholar
  5. [5]
    Park H W, Wensing P M, Kim S. High-speed bounding with the MIT Cheetah 2: Control design and experiments. International Journal of Robotics Research, 2017, 36, 167–192.CrossRefGoogle Scholar
  6. [6]
    WildCat, The World’s Fastest Quadruped Robot, [2017-12-06], Scholar
  7. [7]
    Poulakakis I, Smith J A, Buehler M. On the dynamics of bounding and extensions: Towards the half-bound and gallop gaits. Adaptive Motion of Animals and Machines, 2006, 79–88.CrossRefGoogle Scholar
  8. [8]
    Krasny D P, Orin D E. Evolution of a 3D gallop in a quadrupedal model with biological characteristics. Journal of Intelligent & Robotic Systems, 2010, 60, 59–82.CrossRefzbMATHGoogle Scholar
  9. [9]
    Leonov G, Nijmeijer H, Pogromsky A, Fradkov A. Dynamics and Control of Hybrid Mechanical Systems, World Scientific, Singapore, 2010.CrossRefGoogle Scholar
  10. [10]
    Nanua P. Dynamics of a Galloping Quadruped, Ohio State University, Ohio, USA, 1992.Google Scholar
  11. [11]
    Ringrose R. Self-stabilizing running. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), New Mexico, USA, 1997, 487–493.CrossRefGoogle Scholar
  12. [12]
    Herr H M, McMahon T A. A galloping horse model. International Journal of Robotics Research, 2001, 20, 26–37.CrossRefGoogle Scholar
  13. [13]
    Palmer L R, Orin D E. Intelligent control of high-speed turning in a quadruped. Journal of Intelligent & Robotic Systems, 2010, 58, 47–68.CrossRefzbMATHGoogle Scholar
  14. [14]
    Marhefka D W, Orin D E. Fuzzy control of quadrupedal running. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, USA, 2000, 3063–3069.Google Scholar
  15. [15]
    Wright J, Jordanov I. Intelligent approaches in locomotion–A review. Journal of Intelligent & Robotic Systems, 2015, 80, 255–277.CrossRefGoogle Scholar
  16. [16]
    Chae G, Park J H. Galloping trajectory optimization and control for quadruped robot using genetic algorithm. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Sanya, China, 2007, 1166–1171.Google Scholar
  17. [17]
    Kober J, Bagnell J A, Peters J. Reinforcement learning in robotics: A survey. International Journal of Robotics Research, 2013, 32, 1238–1274.CrossRefGoogle Scholar
  18. [18]
    Vijayakumar S, D’Souza A, Schaal S. Incremental online learning in high dimensions. Neural Computation, 2005, 17, 2602–2634.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Missura M, Behnke S. Online learning of bipedal walking stabilization. KI-Künstliche Intelligenz, 2015, 29, 401–405.CrossRefGoogle Scholar
  20. [20]
    Reiser R F, Peterson M L, Kawcak C E, Mcllwraith C W. Forelimb hoof landing velocities in treadmill trotting and galloping horses. Society for Experimental Mechanics, Portland, USA, 2005.Google Scholar
  21. [21]
    Witte T H, Hirst C V, Wilson A M. Effect of speed on stride parameters in racehorses at gallop in field conditions. Journal of Experimental Biology, 2006, 209, 4389–4397.CrossRefGoogle Scholar
  22. [22]
    Heglund N C, Taylor C R. Speed, stride frequency and energy cost per stride: How do they change with body size and gait? Journal of Experimental Biology, 1988, 138, 301–318.Google Scholar
  23. [23]
    Smith J L, Chung S H, Zernicke R F. Gait-related motor patterns and hindlimb kinetics for cat trot and gallop. Experimental Brain Research, 1993, 94, 308–322.CrossRefGoogle Scholar
  24. [24]
    Liu Q, Chen X, Han B, Luo Z, Luo X. Virtual constraint based control of bounding gait of quadruped robots. Journal of Bionic Engineering, 2017, 14, 218–231.CrossRefGoogle Scholar
  25. [25]
    Fischer M S, Blickhan R. The tri-segmented limbs of therian mammals: Kinematics, dynamics, and self-stabilization–A review. Journal of Experimental Zoology Part A: Comparative Experimental Biology, 2006, 305, 935–952.CrossRefGoogle Scholar
  26. [26]
    Hyun D J, Lee J, Park S I, Kim S. Implementation of trot-to-gallop transition and subsequent gallop on the MIT Cheetah I. International Journal of Robotics Research, 2016, 35, 1627–1650.CrossRefGoogle Scholar

Copyright information

© Jilin University 2018

Authors and Affiliations

  • Qingyu Liu
    • 1
  • Xuedong Chen
    • 1
  • Bin Han
    • 1
  • Zhiwei Luo
    • 1
  • Xin Luo
    • 1
  1. 1.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations