16
F. Angelini et al.
4 Conclusions
In this work we proved experimentally that an ILC-based algorithm can reproduce - when applied to a biobimetic hardware - several behaviors observed when
the central nervous system controls the muscle-skeletal system - namely learning
by repetition, experience-driven shift towards anticipatory behavior, and aftereffect.
Acknowledgments.. This project has been supported by European Union’s Horizon
2020 research and innovation programme under grant agreement 780883 (THING) and
871237 (Sophia), by ERC Synergy Grant 810346 (Natural BionicS) and by the Italian
Ministry of Education and Research (MIUR) in the framework of the CrossLab project
(Departments of Excellence).
References
1. Angelini, F., et al.: Decentralized trajectory tracking control for soft robots interacting with the environment. IEEE Trans. Robot. 34(4), 924–935 (2018)
2. Bernstein, N.A.: Dexterity and Its Development. Psychology Press (2014)
3. Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control.
Control Syst. IEEE 26(3), 96–114 (2006)
4. Cao, J., Liang, W., Zhu, J., Ren, Q.: Control of a muscle-like soft actuator via a
bioinspired approach. Bioinspiration Biom. 13(6), 066005 (2018)
5. Capolei, M.C., Angelidis, E., Falotico, E., Hautop Lund, H., Tolu, S.: A biomimetic
control method increases the adaptability of a humanoid robot acting in a dynamic
environment. Front. Neurorobot. 13, 70 (2019)
6. Della Santina, C., et al.: Controlling soft robots: balancing feedback and feedforward elements. IEEE Robot. Autom. Mag. 24(3), 75–83 (2017)
7. Garabini, M., Santina, C.D., Bianchi, M., Catalano, M., Grioli, G., Bicchi, A.:
Soft robots that mimic the neuromusculoskeletal system. In: Ib´ a˜ nez, J., Gonz´ alezVargas, J., Azor´ ın, J.M., Akay, M., Pons, J.L. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II. BB, vol. 15, pp. 259–263. Springer,
Cham (2017). https://doi.org/10.1007/978-3-319-46669-9 45
8. Hoffmann, J.: Anticipatory behavioral control. In: Butz, M.V., Sigaud, O., G´ erard,
P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol.
2684, pp. 44–65. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-54045002-3 4
9. Lackner, J.R., Dizio, P.: Gravitoinertial force background level affects adaptation
to coriolis force perturbations of reaching movements. J. Neurophysiol. 80(2), 546–
553 (1998)
10. Shadmehr, R., Smith, M.A., Krakauer, J.W.: Error correction, sensory prediction,
and adaptation in motor control. Ann. Rev. Neurosci. 33, 89–108 (2010)
F. Angelini et al.
4 Conclusions
In this work we proved experimentally that an ILC-based algorithm can reproduce - when applied to a biobimetic hardware - several behaviors observed when
the central nervous system controls the muscle-skeletal system - namely learning
by repetition, experience-driven shift towards anticipatory behavior, and aftereffect.
Acknowledgments.. This project has been supported by European Union’s Horizon
2020 research and innovation programme under grant agreement 780883 (THING) and
871237 (Sophia), by ERC Synergy Grant 810346 (Natural BionicS) and by the Italian
Ministry of Education and Research (MIUR) in the framework of the CrossLab project
(Departments of Excellence).
References
1. Angelini, F., et al.: Decentralized trajectory tracking control for soft robots interacting with the environment. IEEE Trans. Robot. 34(4), 924–935 (2018)
2. Bernstein, N.A.: Dexterity and Its Development. Psychology Press (2014)
3. Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control.
Control Syst. IEEE 26(3), 96–114 (2006)
4. Cao, J., Liang, W., Zhu, J., Ren, Q.: Control of a muscle-like soft actuator via a
bioinspired approach. Bioinspiration Biom. 13(6), 066005 (2018)
5. Capolei, M.C., Angelidis, E., Falotico, E., Hautop Lund, H., Tolu, S.: A biomimetic
control method increases the adaptability of a humanoid robot acting in a dynamic
environment. Front. Neurorobot. 13, 70 (2019)
6. Della Santina, C., et al.: Controlling soft robots: balancing feedback and feedforward elements. IEEE Robot. Autom. Mag. 24(3), 75–83 (2017)
7. Garabini, M., Santina, C.D., Bianchi, M., Catalano, M., Grioli, G., Bicchi, A.:
Soft robots that mimic the neuromusculoskeletal system. In: Ib´ a˜ nez, J., Gonz´ alezVargas, J., Azor´ ın, J.M., Akay, M., Pons, J.L. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II. BB, vol. 15, pp. 259–263. Springer,
Cham (2017). https://doi.org/10.1007/978-3-319-46669-9 45
8. Hoffmann, J.: Anticipatory behavioral control. In: Butz, M.V., Sigaud, O., G´ erard,
P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol.
2684, pp. 44–65. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-54045002-3 4
9. Lackner, J.R., Dizio, P.: Gravitoinertial force background level affects adaptation
to coriolis force perturbations of reaching movements. J. Neurophysiol. 80(2), 546–
553 (1998)
10. Shadmehr, R., Smith, M.A., Krakauer, J.W.: Error correction, sensory prediction,
and adaptation in motor control. Ann. Rev. Neurosci. 33, 89–108 (2010)