Skip to main content
SpringerLink
Log in

A Robot Skill Learning Framework Based on Compliant Movement Primitives

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Collaborative robots are increasingly widely used in our lives, and at the same time, the skill learning ability of robots is becoming more and more important. For this reason, a robot skill learning framework based on compliant movement primitives is proposed in this paper. The framework consists of four modules: kinesthetic teaching, task learning, compliant movement primitive library, and task generalization. Specifically, the trajectories are collected from the kinematics of the robot, and the stiffness profiles are collected from the designed variable stiffness interface based on stiffness optimization; then the collected data is optimized, segmented, and learned to create the robot’s compliant movement primitive library; the primitives in the library are adjusted and combined to generate the robot’s desired trajectory and desired stiffness, which are then input into the dynamics-based variable impedance controller; thereafter the controller drives the robot to perform the desired compliant motion and complete various tasks. The framework covers the entire process of robot skill learning and application, and the proposed compliant movement primitives can simultaneously achieve the robot’s trajectory learning and interactive compliance learning. The experiment of the robot learning to press buttons was carried out on a universal 6-DOF collaborative robot. The experimental results prove the effectiveness and safety of the framework and show its application value.

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

Similar content being viewed by others

References

  1. Do, M., Schill, J., Ernesti, J., Asfour, T.: Learn to wipe: A case study of structural bootstrapping from sensorimotor experience. In: Proc. IEEE Int. Conf. Robot. Autom., Hong Kong, China, pp. 1858–1864 (2014)

  2. Yu, X., He, W., Li, Q., Li, Y., Li, B.: Human-robot co-carrying using visual and force sensing. IEEE Trans. Ind. Electron. 68(9), 8657–8666 (2021)

  3. Kormushev, P., Calinon, S., Caldwell, D.G.: Robot motor skill coordination with EM-based Reinforcement Learning. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., Taipei, Taiwan, pp. 3232–3237 (2010)

  4. Burger, B., Maffettone, P.M., Gusev, V.V., Aitchison, C.M., Bai, Y., Wang, X.: A mobile robotic chemist. Nature. 583, 237–241 (2020)

  5. Chernova, S.: Robot learning from demonstration. In: Seel, N.M. (ed.) Encyclopedia of the Sciences of Learning. Springer, Boston (2012)

    Google Scholar 

  6. Kofman, J., Wu, X., Luu, T., Verma, S.: Teleoperation of a robot manipulator using a vision-based human-robot interface. IEEE Trans. Ind. Electron. 52(5), 1206–1219 (2005)

  7. Song, C., Liu, G., Zhang, X., Zang, X., Xu, C., Zhao, J.: Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives. ISA Trans. 97, 325–335 (2020)

  8. Pezent, E., Fani, S., Clark, J., Bianchi, M., O’malley, M.K.: Spatially separating haptic guidance from task dynamics through wearable devices. IEEE Trans. Haptics. 12(4), 581–593 (2019)

  9. You, B., Li, J., Ding, L., Xu, J., Li, W., Li, K., Gao, H.: Semi-autonomous bilateral teleoperation of hexapod robot based on haptic force feedback. J. Intell. Robot. Syst. 91, 583–602 (2018)

  10. Lacki, M., Rossa, C.: Design and control of a 3 degree-of-freedom parallel passive haptic device. IEEE Trans. Haptics. 13(4), 720–732 (2020)

  11. Müller, D., Veil, C., Seidel, M., Sawodny, O.: One-Shot kinesthetic programming by demonstration for soft collaborative robots. Mechatronics. 70, 102418 (2020)

  12. Lin, H.I.: Learning on robot skills: Motion adjustment and smooth concatenation of motion blocks. Eng. Appl. Artif. Intell. 91, 103619 (2020)

  13. Manschitz, S., Kober, J., Gienger, M., Peters, J.: Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations. Robot. Auton. Syst. 74, 97–107 (2015)

  14. Vidaković, J., Jerbić, B., Šekoranja, B., Švaco, M., Šuligoj, F.: Learning from demonstration based on a classification of task parameters and trajectory optimization. J. Intell. Robot. Syst. 99, 261–275 (2020)

  15. Deniša, M., Gams, A., Ude, A., Petrič, T.: Learning compliant movement primitives through demonstration and statistical generalization. IEEE-ASME Trans. Mechatron. 21(5), 2581–2594 (2016)

  16. Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Trans. Robot. 27(5), 943–957 (2011)

  17. Wang, Q., Jiao, W., Yu, R., Johnson, M.T., Zhang, Y.M.: Virtual reality robot-assisted welding based on human intention recognition. IEEE Trans. Autom. Sci. Eng. 17(2), 799–808 (2020)

  18. Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25, 328–373 (2013)

  19. Shyam, R.A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., Neumann, G.: Improving Local Trajectory Optimization using Probabilistic Movement Primitives. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., Macau, China, pp. 2666–2671 (2019)

  20. Paraschos, A., Daniel, C., Peters, J., Neumann, G.: Using probabilistic movement primitives in robotics. Auton. Robot. 42(3), 1–23 (2018)

  21. Kulvicius, T., Ning, K., Tamosiunaite, M., Wörgötter, F.: Joining movement sequences: modified dynamic movement primitives for robotics applications exemplified on handwriting. IEEE Trans. Robot. 28(1), 145–157 (2012)

  22. Colome, A., Torras, C.: Dimensionality reduction for dynamic movement primitives and application to bimanual manipulation of clothes. IEEE Trans. Robot. 34(3), 602–615 (2018)

  23. Zhao, T., Deng, M., Li, Z., Hu, Y.: Cooperative manipulation for a mobile dual-arm robot using sequences of dynamic movement primitives. IEEE Trans. Cogn. Dev. Syst. 12(1), 18–29 (2020)

  24. Clark, L., Shirinzadeh, B., Tian, Y., Yao, B.: Development of a passive compliant mechanism for measurement of Micro/nanoscale planar 3-DOF motions. IEEE-ASME Trans. Mechatron. 31(3), 1222–1232 (2016)

  25. Xing, D., Lv, Y., Liu, S., Xu, D., Liu, F.: Efficient insertion of multiple objects parallel connected by passive compliant mechanisms in precision assembly. IEEE Trans. Ind. Inform. 15(9), 4878–4887 (2019)

  26. Duan, J., Ou, Y., Xu, S., Liu, M.: Sequential learning unification controller from human demonstrations for robotic compliant manipulation. Neurocomputing. 366, 35–45 (2019)

  27. Liu, N., Zhou, X., Liu, Z., Wang, H., Cui, L.: Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism. Assem. Autom. 40(6), 895–904 (2020)

  28. Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.: Interface Design of a Physical Human-Robot Interaction System for human impedance adaptive skill transfer. IEEE Trans. Autom. Sci. Eng. 15(1), 329–340 (2018)

  29. Kim, M.J., Beck, F., Ott, C., Albu-Schäffer, A.: Model-free friction observers for flexible joint robots with torque measurements. IEEE Trans. Robot. 35(6), 1508–1515 (2019)

  30. Xiao, J., Zeng, F., Zhang, Q., Liu, H.: Research on the forcefree control of cooperative robots based on dynamic parameters identification. Ind. Robot. 46(4), 499–509 (2019)

  31. Hogan, N.: Impedance Control: an Approach to Manipulation. In: American Control Conference, pp. 304–313 (1984)

  32. Schaal, S., Atkeson, C.G., Vijayakumar, S.: Scalable techniques from nonparametric statistics for real time robot learning. Appl. Intell. 17(1), 49–60 (2002)

  33. Muelling, K., Kober, J., Peters, J.: Learning Table Tennis with a Mixture of Motor Primitives. In: Proc. Int. Conf. on Humanoid Robots, pp. 411–416 (2010)

  34. Schaal, S.: Dynamic movement primitives -a framework for motor control in humans and humanoid robotics. In: Kimura, H., Tsuchiya, K., Ishiguro, A., Witte, H. (eds.) Adaptive Motion of Animals and Machines. Springer, Tokyo (2006)

    Google Scholar 

  35. Wächter, M., Asfour, T.: Hierarchical Segmentation of Manipulation Actions Based on Object Relations and Motion Characteristics. In: Proc. Int. Conf. on Adv. Robot., pp. 549–556 (2015)

  36. Kim, B., Park, J., Park, S., Kang, S.: Impedance learning for robotic contact tasks using natural actor-critic algorithm. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 40(2), 433–443 (2010)

  37. Kronander, K., Billard, A.: Learning compliant manipulation through kinesthetic and tactile human-robot interaction. IEEE Trans. Haptics. 7(3), 367–380 (2014)

  38. Kronander, K., Billard, A.: Stability considerations for variable impedance control. IEEE Trans. Robot. 32(5), 1298–1305 (2016)

Download references

Availability of Data and Material

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

This work is supported in part by National Natural Science Foundation of China under Grant 91948301 and Grant 51721003.

Author information

Authors and Affiliations

  1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, China

    Saixiong Dou, Juliang Xiao, Wei Zhao, Hang Yuan & Haitao Liu

Authors
  1. Saixiong Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juliang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haitao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Saixiong Dou and Juliang Xiao; Methodology: Saixiong Dou, Juliang Xiao and Haitao Liu; Formal analysis and investigation: Saixiong Dou, Wei Zhao and Hang Yuan; Writing- original draft preparation: Saixiong Dou; Writing- review and editing: Saixiong Dou, Hang Yuan and Juliang Xiao; Funding acquisition: Juliang Xiao and Haitao Liu.

Corresponding author

Correspondence to Juliang Xiao.

Ethics declarations

Competing Interests

The authors declare that they have no competing interests.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dou, S., Xiao, J., Zhao, W. et al. A Robot Skill Learning Framework Based on Compliant Movement Primitives. J Intell Robot Syst 104, 53 (2022). https://doi.org/10.1007/s10846-022-01605-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01605-4

Keywords

Search

Navigation