Abstract
Continuous soft robots are becoming more and more widespread in applications, due to their increased safety and flexibility in critical applications. The possibility of having soft robots that are able to change their stiffness in selected parts can help in situations where higher forces need to be applied. This paper describes a theoretical framework for learning the desired stiffness characteristics of the robot from multiple demonstrations. The framework is based on a statistical mathematical model for encoding the motion of a continuous manipulator, coupled with an optimal control strategy for learning the best impedance parameters of the manipulator.
This work was partially supported by the STIFF-FLOP European project under contract FP7-ICT-287728.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jiang, A., Xynogalas, G., Dasgupta, P., Althoefer, K., Nanayakkara, T.: Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2922–2927 (2012)
Jiang, A., Ataollahi, A., Althoefer, K., Dasgupta, P., Nanayakkara, T.: A variable stiffness joint by granular jamming. In: ASME Intl Design Engineering Technical Conf. & Computers and Information in Engineering Conf. (IDETC/CIE), pp. 267–275 (2012)
Cianchetti, M., Ranzani, T., Gerboni, G., De Falco, I., Laschi, C., Menciassi, A.: STIFF-FLOP surgical manipulator: mechanical design and experimental characterization of the single module. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3567–3581 (2013)
Cianchetti, M., Ranzani, T., Gerboni, G., Nanayakkara, T., Althoefer, K., Dasgupta, P., Menciassi, A.: Soft robotics technologies to address shortcomings in today’s minimally invasive surgery: the stiff-flop approach. Soft Robotics 1(2), 122–131 (2014)
Calinon, S., D’halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G.: Learning and reproduction of gestures by imitation: An approach based on hidden Markov model and Gaussian mixture regression. IEEE Robotics and Automation Magazine 17, 44–54 (2010)
Ijspeert, A., Nakanishi, J., Pastor, P., Hoffmann, H., Schaal, S.: Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Computation 25(2), 328–373 (2013)
Calinon, S., Li, Z., Alizadeh, T., Tsagarakis, N.G., Caldwell, D.G.: Statistical dynamical systems for skills acquisition in humanoids. In: Proc. IEEE Intl Conf. on Humanoid Robots (Humanoids), Osaka, Japan, pp. 323–329 (2012)
Calinon, S., Bruno, D., Caldwell, D.G.: A task-parameterized probabilistic model with minimal intervention control. In: Proc. IEEE Intl Conf. on Robotics and Automation (ICRA), Hong Kong, China, pp. 3339–3344, May-June 2014
Calinon, S., Billard, A.G.: Recognition and reproduction of gestures usinga probabilistic framework combining PCA, ICA and HMM. In: Proc. Intl Conf. on Machine Learning (ICML), Bonn, Germany, pp. 105–112, August 2005
Lee, D., Ott, C.: Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots 31(2), 115–131 (2011)
Astrom, K.J., Murray, R.M.: Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, Princeton (2008)
Medina, J.R., Lee, D., Hirche, S.: Risk-sensitive optimal feedback control for haptic assistance. In: IEEE Intl Conf. on Robotics and Automation (ICRA), pp. 1025–1031, May 2012
Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5, 1226–1235 (2002)
Flash, T., Hochner, B.: Motor primitives in vertebrates and invertebrates. Current Opinion in Neurobiology 15(6), 660–666 (2005)
Zelman, I., Titon, M., Yekutieli, Y., Hanassy, S., Hochner, B., Flash, T.: Kinematic decomposition and classification of octopus arm movements. Frontiers in Computational Neuroscience 7(60) (2013)
Malekzadeh, M.S., Calinon, S., Bruno, D., Caldwell, D.G.: Learning by imitation with the STIFF-FLOP surgical robot: A biomimetic approach inspired by octopus movements. Robotics and Biomimetics, Special Issue on Medical Robotics 1, 1–15 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bruno, D., Calinon, S., Malekzadeh, M.S., Caldwell, D.G. (2015). Learning the Stiffness of a Continuous Soft Manipulator from Multiple Demonstrations. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R. (eds) Intelligent Robotics and Applications. Lecture Notes in Computer Science(), vol 9246. Springer, Cham. https://doi.org/10.1007/978-3-319-22873-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-22873-0_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22872-3
Online ISBN: 978-3-319-22873-0
eBook Packages: Computer ScienceComputer Science (R0)