Skip to main content
SpringerLink
Log in

Parameters auto-tuning for biped robots in whole-body stabilization and active impedance control applications

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This work proposes a parameters auto-tuning strategy for biped locomotion in whole-body stabilization control (inverse kinematics based and inverse dynamics based) and active impedance control based on Bayesian optimization(BO). Using the domain knowledge, the parameter space is divided into three sub-spaces and optimized by decoupling BO and alternating BO algorithms. The effectiveness of the proposed method is demonstrated in simulation using a torque-controlled biped robot that we developed. The 32 control parameters are tuned in less than 400 evaluations. In addition, the auto-tuned parameters are robust to different top-level velocity inputs and show compliant behavior with balance in push recovery scenarios. To the best of our knowledge, this is the first work to automatically tune the parameters of the three controllers (inverse kinematics, inverse dynamics and active impedance control) jointly.

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Kajita S, Kanehiro F, Kaneko K, Yokoi K, Hirukawa H (2001) The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation. In: Proceedings 2001 IEEE/RSJ international conference on intelligent robots and systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180), vol 1, IEEE, Maui, HI, USA, pp 239–246. https://doi.org/10.1109/IROS.2001.973365, http://ieeexplore.ieee.org/document/973365/ Accessed 2021-11-11

  2. Stephens BJ, Atkeson CG (2010) Push Recovery by stepping for humanoid robots with force controlled joints. In: 2010 10th IEEE-RAS international conference on humanoid robots, IEEE, Nashville, TN, USA, pp 52–59. https://doi.org/10.1109/ICHR.2010.5686288. http://ieeexplore.ieee.org/document/5686288/ Accessed 2021-11-11

  3. Dong S, Yuan Z, Yu X, Sadiq MT, Zhang J, Zhang F, Wang C (2020) Flexible model predictive control based on multivariable online adjustment mechanism for robust gait generation. International Journal of Advanced Robotic Systems 17(1):172988141988729. https://doi.org/10.1177/1729881419887291. Accessed 2020-05-20

    Article  Google Scholar 

  4. Winkler AW, Bellicoso CD, Hutter M, Buchli J (2018) Gait and trajectory optimization for legged systems through phase-based end-effector parameterization. IEEE Robot Autom Lett 3(3):1560–1567. https://doi.org/10.1109/LRA.2018.2798285. Accessed 2021-11-11

    Article  Google Scholar 

  5. Hosseinmemar A, Baltes J, Anderson J, Lau MC, Lun CF, Wang Z (2019) Closed-loop push recovery for inexpensive humanoid robots. Appl Intell 49(11):3801–3814. https://doi.org/10.1007/s10489-019-01446-zhttps://doi.org/10.1007/s10489-019-01446-z. Accessed 2021-11-11

    Article  Google Scholar 

  6. Bledt G, Kim S (2019) Implementing regularized predictive control for simultaneous real-time footstep and ground reaction force optimization. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, Macau, China, pp 6316–6323. https://doi.org/10.1109/IROS40897.2019.8968031, https://ieeexplore.ieee.org/document/8968031/ Accessed 2021-11-11

  7. Choi Y, Kim D, Oh Y, You B-J (2007) Posture/Walking control for humanoid robot based on kinematic resolution of CoM jacobian with embedded motion. IEEE Trans Robot 23(6):1285–1293. https://doi.org/10.1109/TRO.2007.904907. Accessed 2021-11-11

    Article  Google Scholar 

  8. Stephens BJ, Atkeson CG (2010) Dynamic balance force control for compliant humanoid robots. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, IEEE, Taipei, pp 1248–1255. https://doi.org/10.1109/IROS.2010.5648837, http://ieeexplore.ieee.org/document/5648837/ Accessed 2021-11-11

  9. Caron S, Kheddar A, Tempier O (2019) Stair climbing stabilization of the HRP-4 humanoid robot using whole-body admittance control. In: 2019 international conference on robotics and automation (ICRA), IEEE, Montreal, QC, Canada, pp 277–283. https://doi.org/10.1109/ICRA.2019.8794348, https://ieeexplore.ieee.org/document/8794348/ Accessed 2021-11-11

  10. Kashyap AK, Parhi DR, Kumar S (2020) Dynamic stabilization of NAO humanoid robot based on whole-body control with simulated annealing. International Journal of Humanoid Robotics 17 (03):2050014. https://doi.org/10.1142/S0219843620500140. Accessed 2021-11-11

    Article  Google Scholar 

  11. Boyd S, Boyd SP, Vandenberghe L (2004) Convex Optimization. Cambridge University Press

  12. Deng B (2021) Efficient spike-Driven learning with dendritic event-Based processing. Front Neurosci 15:15

    Google Scholar 

  13. Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 15

  14. Pozna C, Precup R-E, Horvath E, Petriu EM Hybrid Particle Filter-Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems. IEEE Transactions on Fuzzy Systems, 14

  15. Zamfirache IA (2022) Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system. Inf Sci, 22

  16. Jaisumroum N, Chotiprayanakul P, Limnararat S (2016) Self-tuning control with neural network for robot manipulator. In: 2016 16th international conference on control, automation and systems (ICCAS), IEEE, Gyeongju, South Korea, pp 1073–1076. https://doi.org/10.1109/ICCAS.2016.7832443, http://ieeexplore.ieee.org/document/7832443/http://ieeexplore.ieee.org/document/7832443/ Accessed 2021-11-11

  17. Roveda L, Pallucca G, Pedrocchi N, Braghin F, Tosatti LM (2018) Iterative learning procedure with reinforcement for high-accuracy force tracking in robotized tasks. IEEE Trans Ind Inform 14 (4):1753–1763. https://doi.org/10.1109/TII.2017.2748236. Accessed 2021-11-11

    Article  Google Scholar 

  18. Dallali H, Kormushev P, Li Z, Caldwell D (2012) On global optimization of walking gaits for the compliant humanoid robot, COMAN Using Reinforcement Learning, vol 12. https://doi.org/10.2478/cait-2012-0020. Accessed 2021-11-11

  19. Niehaus C, Röfer T, Laue T (2007) Gait optimization on a humanoid robot using particle swarm optimization. In: Proceedings of the Second workshop on humanoid soccer robots in conjunction with The, pp 1–7. sn

  20. Idris I. (2015) A combined negative selection algorithm–particle swarm optimization for an email spam detection system. Eng Appl Artif Intell, 12

  21. Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: International conference on learning and intelligent optimization, Springer, pp 507–523

  22. Zhao J, Han L, Wang L, Yu Z (2016) The fuzzy PID control optimized by genetic algorithm for trajectory tracking of robot arm. In: 2016 12th World congress on intelligent control and automation (WCICA), IEEE, Guilin, China, pp 556–559. https://doi.org/10.1109/WCICA.2016.7578443, http://ieeexplore.ieee.org/document/7578443/ Accessed 2021-11-11

  23. Brochu E, Cora VM, de Freitas N (2010) A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv:1012.2599 [cs]. Accessed 2021-09-26

  24. Roveda L, Forgione M, Piga D (2020) Robot control parameters auto-tuning in trajectory tracking applications. Control Engineering Practice 101:104488. https://doi.org/10.1016/j.conengprac.2020.104488https://doi.org/10.1016/j.conengprac.2020.104488. Accessed 2021-10-01

    Article  Google Scholar 

  25. Calandra R, Seyfarth A, Peters J, Deisenroth MP (2014) An experimental comparison of Bayesian optimization for bipedal locomotion. In: 2014 IEEE international conference on robotics and automation (ICRA), IEEE, Hong Kong, China, pp 1951–1958. https://doi.org/10.1109/ICRA.2014.6907117, http://ieeexplore.ieee.org/document/6907117/http://ieeexplore.ieee.org/document/6907117/ Accessed 2021-10-13

  26. Yeganegi MH, Khadiv M, Moosavian SAA, Zhu J-J, Del Prete A, Righetti L (2019) Robust humanoid locomotion using trajectory optimization and sample-efficient learning *. In: 2019 IEEE-RAS 19th international conference on humanoid robots (Humanoids), IEEE, Toronto, ON, Canada, pp 170–177. https://doi.org/10.1109/Humanoids43949.2019.9035003, https://ieeexplore.ieee.org/document/9035003/ Accessed 2021-09-25

  27. Antonova R, Rai A, Atkeson CG (2016) Sample efficient optimization for learning controllers for bipedal locomotion. In: 2016 IEEE-RAS 16th international conference on humanoid robots (Humanoids), IEEE, Cancun, Mexico, pp 22–28. https://doi.org/10.1109/HUMANOIDS.2016.7803249, http://ieeexplore.ieee.org/document/7803249/ Accessed 2021-10-13

  28. Rai A, Antonova R, Song S, Martin W, Geyer H, Atkeson C (2018) Bayesian optimization using domain knowledge on the ATRIAS biped. In: 2018 IEEE international conference on robotics and automation (ICRA), IEEE, Brisbane, QLD, pp 1771–1778. https://doi.org/10.1109/ICRA.2018.8461237, https://ieeexplore.ieee.org/document/8461237/https://ieeexplore.ieee.org/document/8461237/ Accessed 2021-10-13

  29. Charbonneau M, Modugno V, Nori F, Oriolo G, Pucci D, Ivaldi S (2018) Learning robust task priorities of QP-based whole-body torque-controllers. In: 2018 IEEE-RAS 18th international conference on humanoid robots (Humanoids), IEEE, Beijing, China, pp 1–9. https://doi.org/10.1109/HUMANOIDS.2018.8624995, https://ieeexplore.ieee.org/document/8624995/ Accessed 2021-10-13

  30. Yuan K, Chatzinikolaidis I, Li Z (2019) Bayesian optimization for whole-body control of high-degree-of-freedom robots through reduction of dimensionality. IEEE Robot Autom Lett 4(3):2268–2275. https://doi.org/10.1109/LRA.2019.2901308. Accessed 2021-09-26

    Article  Google Scholar 

  31. Semini C, Barasuol V, Boaventura T, Frigerio M, Focchi M, Caldwell DG, Buchli J (2015) Towards versatile legged robots through active impedance control. The Int J Robot Res 34(7):1003–1020. https://doi.org/10.1177/0278364915578839. Accessed 2021-07-08

    Article  Google Scholar 

  32. Zhang J, Yuan Z, Dong S, Sadiq MT, Zhang F, Li J (2020) Structural design and kinematics simulation of hydraulic biped robot. Appl Sci 10(18):6377. https://doi.org/10.3390/app10186377. Accessed 2020-10-07

    Article  Google Scholar 

  33. Englsberger J, Ott C, Roa MA, Albu-Schaffer A, Hirzinger G (2011) Bipedal walking control based on Capture Point dynamics. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, IEEE, San Francisco, CA, pp 4420–4427. https://doi.org/10.1109/IROS.2011.6094435, http://ieeexplore.ieee.org/document/6094435/http://ieeexplore.ieee.org/document/6094435/ Accessed 2021-12-15

  34. Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25

  35. Bull AD (2011) Convergence rates of efficient global optimization algorithms. Journal of Machine Learning Research 12(10)

  36. Hereid A, Ames AD (2017) FROST: Fast robot optimization and simulation toolkit. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, Vancouver, BC, pp 719–726. https://doi.org/10.1109/IROS.2017.8202230, http://ieeexplore.ieee.org/document/8202230/ Accessed 2021-11-12

Download references

Acknowledgements

The authors would like to acknowledge the financial support provided by the Natural Science Basic Research Plan in Shaanxi Province of China (program no.2018JQ6014), and the Science and Technology program of Gansu Province (program no.20JR5RA483).

Funding

This work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (program no.2018JQ6014), and the Science and Technology program of Gansu Province (program no.20JR5RA483).

Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Dongxiang Road 1, Xi’an, 710129, Shaanxi, People’s Republic of China

    Jingchao Li, Zhaohui Yuan, Jian Kang, Pengfei Yang, Jianrui Zhang & Yingxing Li

  2. School of electrical and control engineering, Shaanxi University of Science and Technology, Weiyang University Park, Xi’an, 710021, Shaanxi, People’s Republic of China

    Sheng Dong

Authors
  1. Jingchao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaohui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sheng Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pengfei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianrui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yingxing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jingchao Li: Conceptualization, methodology, and validation. Zhaohui Yuan: Project administration and supervision. Sheng Dong: Investigation and software. Jian Kang: Writing-Review & Editing. Pengfei Yang: Visualization. Jianrui Zhang: Funding Acquisition. Yingxing Li: Writing-Review & Editing.

Corresponding author

Correspondence to Jingchao Li.

Ethics declarations

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Zhaohui Yuan and Sheng Dong are authors contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Yuan, Z., Dong, S. et al. Parameters auto-tuning for biped robots in whole-body stabilization and active impedance control applications. Appl Intell 53, 7848–7861 (2023). https://doi.org/10.1007/s10489-022-03792-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03792-x

Keywords

Search

Navigation