Skip to main content

Using inverse learning for controlling bionic robotic fish with SMA actuators


In this study, we develop an untethered bionic soft robotic fish for swimming motion. The body of the fish is molded using soft silicone rubber, and we utilize shape memory alloy wires for its actuators. Its lightness and flexibility allow the robotic fish to generate biomimetic swimming motions. Due to the complexity of mathematically modeling the robot’s swimming dynamics, building a realistic simulator is prohibitively difficult. Hence, in this study, we introduce inverse learning for a feedforward neural network to generate control parameters for realizing desired swimming motions and subsequently utilize the neural network for real-time control. In this paper, we report on the electro-mechanical structure of our robotic fish and the experiment of the neuro-controller.

Graphical abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Data availability

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


  1. C. Lee et al., Soft robot review. Int. J. Control Autom. Syst. 15(1), 3–15 (2017)

    Article  Google Scholar 

  2. B. He, Z. Wang, H. Tang, Review of soft robot. J. Tongji Univ. 42(10), 1596–1603 (2014)

    Google Scholar 

  3. S. Saito et al., Development of a soft actuator using a photocurable ionic gel. J. Micromech. Microeng. 19(3), 035005 (2009)

    Article  Google Scholar 

  4. Y. Hara et al., Development of novel self-oscillating molecular robot fueled by organic acid, in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. (IEEE, St. Louis, 2009)

    Google Scholar 

  5. T. Li et al., Fast-moving soft electronic fish. Sci. Adv. 3(4), e1602045 (2017)

    Article  Google Scholar 

  6. G. Li et al., Self-powered soft robot in the Mariana Trench. Nature 591(7848), 66–71 (2021)

    CAS  Article  Google Scholar 

  7. M. Calisti et al., An octopus-bioinspired solution to movement and manipulation for soft robots. Bioinspir. Biomim. 6(3), 036002 (2011)

    CAS  Article  Google Scholar 

  8. S. Seok et al., Meshworm: a peristaltic soft robot with antagonistic nickel titanium coil actuators. IEEE/ASME Trans. Mechatron. 18(5), 1485–1497 (2012)

    Article  Google Scholar 

  9. T. Nakamura, N. Saga, K. Yaegashi, Development of a pneumatic artificial muscle based on biomechanical characteristics, in IEEE International Conference on Industrial Technology, 2003. (IEEE, Maribor, 2003)

    Google Scholar 

  10. C.-P. Chou, B. Hannaford, Measurement and modeling of McKibben pneumatic artificial muscles. IEEE Trans. Robot. Autom. 12(1), 90–102 (1996)

    Article  Google Scholar 

  11. E. Steltz et al., Jsel: jamming skin enabled locomotion, in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. (IEEE, St. Louis, 2009)

    Google Scholar 

  12. C.D. Onal, D. Rus, Autonomous undulatory serpentine locomotion utilizing body dynamics of a fluidic soft robot. Bioinspir. Biomim. 8(2), 026003 (2013)

    Article  Google Scholar 

  13. Y. Nakabo, Biomimetic soft robot using artificial muscle, in tutorial’Electro-Active Polymer for Use in Robotics’, in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Sendai, 2004. (2004)

  14. K. Ogawa et al., A snake-like swimming robot with an artificial muscle. Trans. Soc. Instrum Control Eng 42(1), 80–89 (2006)

    Article  Google Scholar 

  15. Z. Mao, T. Iizuka, S. Maeda, Bidirectional electrohydrodynamic pump with high symmetrical performance and its application to a tube actuator. Sens. Actuators A 332, 113168 (2021)

    CAS  Article  Google Scholar 

  16. H. Sawada, Tactile display using the micro-vibration of shape-memory alloy wires and its application to tactile interaction systems, in Pervasive Haptics. (Springer, Tokyo, 2016), pp. 57–77

    Chapter  Google Scholar 

  17. X. Chen, H. Shigemune, H. Sawada, An untethered bionic robotic fish using SMA actuators, in 2020 IEEE International Conference on Mechatronics and Automation (ICMA). (IEEE, Beijing, 2020)

    Google Scholar 

  18. X. Chen et al., An untethered soft robotic fish using SMA wires and its performance analysis. Int. J. Mechatron. Autom. 8(4), 229–240 (2021)

    Article  Google Scholar 

  19. M. Sfakiotakis, D.M. Lane, J.B.C. Davies, Review of fish swimming modes for aquatic locomotion. IEEE J. Oceanic Eng. 24(2), 237–252 (1999)

    Article  Google Scholar 

  20. J. Shintake et al., Soft biomimetic fish robot made of dielectric elastomer actuators. Soft Rob. 5(4), 466–474 (2018)

    Article  Google Scholar 

  21. FISH 03 3D MODEL. 2018/09/16; Available at

  22. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

Download references


This work was supported by JSPS Grants-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) 18H05473 and 18H05895 and by JSPS Grant-in-Aid for Scientific Research (B) 20H04214.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kewei Ning.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ning, K., Hartono, P. & Sawada, H. Using inverse learning for controlling bionic robotic fish with SMA actuators. MRS Advances (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: