Skip to main content
SpringerLink
Log in

Intelligent Fuzzy Q-Learning Control of Humanoid Robots

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

Included in the following conference series:

Abstract

In this paper, a design methodology for enhancing the stability of humanoid robots is presented. Fuzzy Q-Learning (FQL) is applied to improve the Zero Moment Point (ZMP) performance by intelligent control of the trunk of a humanoid robot. With the fuzzy evaluation signal and the neural networks of FQL, biped robots are dynamically balanced in situations of uneven terrains. At the mean time, expert knowledge can be embedded to reduce the training time. Simulation studies show that the FQL controller is able to improve the stability as the actual ZMP trajectories become close to the ideal case.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Huang, Q., Yokoi, K., Kajita, S., Kaneko, S., Rrai, H., Koyachi, N., Tanie, K.: Planning Walking Patterns for A Biped Robot. IEEE Trans. Robotics and Automation 17, 280–289 (2001)

    Article  Google Scholar 

  2. Vukobratovic, M.: Zero-Moment Point-Thirty Five Yeas of Its Life. International Jounal of Humanoid Robotics 1, 157–173 (2001)

    Article  Google Scholar 

  3. Juang, J.G.: Fuzzy Neural Network Approaches for Robotic Gait Synthesis. IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics 30, 594–601 (2000)

    Article  Google Scholar 

  4. Ogino, M., Katoh, Y., Aono, M., Asada, M., Hosoda, K.: Reinforcement Learning of Humanoid Rhythmic Walking Parameters Based on Visual Information. Advanced Robotics 18, 677–697 (2004)

    Article  Google Scholar 

  5. Zhou, C.: Robot Learning with GA-based Fuzzy Reinforcement Learning Agents. Information Science 145, 45–68 (2002)

    Article  MATH  Google Scholar 

  6. Watkins, C.J.C.H.: Learning from Delayed Rewards.PhD Thesis, Cambridge University (1989)

    Google Scholar 

  7. Jang, J.S.R.: ANFIS: Adaptive-network-based Fuzzy Inference System. IEEE Trans. System, Man and Cybernetics 23, 665–684 (1993)

    Article  Google Scholar 

  8. Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, New Jersey (1997)

    MATH  Google Scholar 

  9. Er, M.J., Deng, C.: Online Tuning of Fuzzy Inference Systems Using Dynamic Fuzzy QLearning. IEEE Trans on Systems, Man and Cybernetics, Part B 34, 1478–1489 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Center, 50 Nanyang Drive, 7th Storey, Research TechnoPlaza, Boarder X Block, Singapore, 637533

    Meng Joo Er & Yi Zhou

Authors
  1. Meng Joo Er
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Er, M.J., Zhou, Y. (2005). Intelligent Fuzzy Q-Learning Control of Humanoid Robots. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_34

Download citation

  • DOI: https://doi.org/10.1007/11427469_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation