Abstract
The hand is one of the most complex and fascinating organs of the human body. We can powerfully squeeze objects, but we are also capable of manipulating them with great precision and dexterity. On the other hand, the arm, with its redundant joints, is in charge of reaching the object by determining the hand pose during preshaping. The complex motion and task execution of the upper-limb system may lead us to think that the control requires a very significant brain effort. As a matter of fact, neuroscience studies demonstrate that humans simplify planning and control using a combination of primitives, which the brain modulates to produce hand configurations and force patterns for the purpose of grasping and manipulating different objects. This concept can be transferred to robotic systems, allowing control within a space of lower dimension. The lower number of parameters characterizing the system allows for embodying the control in machine learning frameworks, reproducing a sort of human-like cognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kapandji, I. A. (1970). The physiology of the joints. Upper limb (Vol. 1, 2nd ed., pp. 146–202). London: E. and S. Livingstone.
Tubiana, R. (1981). Architecture and function of the hand. In R. Tubiana (Ed.), The Hand (Vol. 1, pp. 19–93). Philadelphia, PA: Saunders.
Soechting, J. F., & Flanders, M. (1997). Flexibility and repeatability of finger movements during typing: Analysis of multiple degrees of freedom. Journal of Computing Neuroscience, 4, 29–46.
Lemon, R. N. (1999). Neural control of dexterity: What has been achieved? Experimental Brain Research, 128, 6–12.
Schieber, M. (1990). How might the motor cortex individuate movements? Trends Neuroscience, 13, 440–445.
Schieber, M. (1995). Muscular production of individuated finger movements: The roles of extrinsic finger muscles. Journal of Neuroscience, 15, 284–297.
Napier, J. R. (1956). The prehensile movements of the human hand. Journal Bone and Joint Surgery, 38B, 902–913.
Johannson, R. S., & Cole, K. J. (1992). Sensory-motor coordination during grasping and manipulative actions. Current Opinion in Neurology, 2, 815–823.
Kamakura N., Matsuo M., Ishii H., Mitsuboshi F., & Miura Y. Patterns of static prehension in normal hands. The American Journal of Occupational Therapy, 7, 437–445.
Elliot, J. M., & Connolly, K. J. A. (1984). Classification of manipulative hand movements. Developmental Medicine & Child Neurology, 26, 283–296.
Klatzky, R. L., Pellegrino, J., McCloskey, B., Doherty, S., & Smith, T. (1987). Knowledge about hand shaping and knowledge about objects. Journal of Motor Behavior, 19, 187–213.
Cutkosky, M. R., & Howe, R. D. (1990). Human grasp choice and robotic grasp analysis. In S. T. Venkataraman & T. Iberall (Eds.), Dextrous robot hands (pp. 5–31). New York: Springer.
Santello, M., Flanders, M., & Soechting, J. F. (1998). Postural hand synergies for tool use. The Journal of Neuroscience, 18, 10105–10115.
Mason, C. R., Gomez, J. E., & Ebner, T. J. (2001). Hand synergies during reach-to-grasp. Journal of Neurophysiology, 86, 2896–2910.
Ficuciello F., Palli G., Melchiorri C., & Siciliano B. (2013). A model-based strategy for mapping human grasps to robotic hands using synergies. In Proceedings 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.
Palli, G., Melchiorri, C., Vassura, G., Berselli, G., Pirozzi, S., Natale, C., De Maria, G., & May, C. (2012). Innovative technologies for the next generation of robotic hands. In B. Siciliano (Ed.), Advanced Bimanual Manipulation. Springer Tracts in Advanced Robotics (Vol. 80, pp. 173–218). Springer.
Grebenstein, M., Chalon, M., Hirzinger, G., & Siegwart, R. (2010). A method for hand kinematics designers 7 billion perfect hands. In Proceedings 1st International Conference on Applied Bionics and Biomechanics (pp. 357–362). Venice, Italy.
Siciliano, B., & Khatib, O. (Eds.). (2008). Springer Handbook of Robotics (2nd ed.). Springer.
Ficuciello, F., Federico, A., Lippiello, V., & Siciliano, B. (2017). Synergies evaluation of the SCHUNK S5FH for grasping control. Springer Proceedings in Advanced Robotics, 4, 225–233.
Schunk hand webpage. http://www.schunk-modular-robotics.com/en/home/products/servo-electric-5-finger-gripping-hand-svh.html.
Ficuciello, F., Palli, G., Melchiorri, C., Siciliano, B. (2011). Experimental evaluation of postural synergies during reach to grasp with the UB Hand IV. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1775–1780). San Francisco.
Geng, T., Lee, M., & Hulse, M. (2011). Transferring human grasping synergies to a robot. Mechatronics, 21(1), 272–284.
Sun, S., Rosales, C., & Suarez, R. (2010). Study of coordinated motions of the human hand for robotic applications. In Proceedings IEEE International Conference on Robotics and Automation (pp. 776–781). Anchorage, Alaska.
Gioioso, G., Salvietti, G., Malvezzi, M., & Prattichizzo, P. (2011). Mapping synergies from human to robotic hands with dissimilar kinematics: An object based approach. In IEEE International Conference on Robotics and Automation, Workshop on Manipulation Under Uncertainty. Shangai.
Ficuciello, F., Zaccara, D., & Siciliano, B. (2016). Learning grasps in a synergy-based framework. In Springer Proceedings in Advanced Robotics (Vol. 1, pp. 125–135). Cham.
KUKA Lightweight Robot 4+ webpage. https://www.kukakore.com/wp-content/uploads/2012/07/KUKA_LBR4plus_ENLISCH.pdf.
Xsens-MVN webpage. https://www.xsens.com/products/xsens-mvn.
Giorgino, T. (2009). Computing and visualizing dynamic time warping alignments in R: The dtw package. Journal of statistical Software, 31(7), 1–24.
Ramsay, J. O., & Silverman, B. W. (2005). Functional Data Analysis. Springer.
Jacques, J., & Preda, C. (2004). Model-based clustering for multivariate functional data. Computational Statistics & Data Analysis, 71, 92–106.
Happ, C. (2015). Multivariate functional principal component analysis for data observed on different (dimensional) domains. Journal of the American Statistical Association.
Berrendero, J., Justel, A., & Svarc, M. (2011). Principal components for multivariate functional data. Computational Statistics & Data Analysis, 55(9), 2619–2634.
Ficuciello, F., Zaccara, D., & Siciliano, B. (2016). Synergy-based policy improvement with path integrals for anthropomorphic hands. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2016) (pp. 1940–1945).
Theodorou, E., Buchli, J., & Schaal, S. (2010). A generalized path integral control approach to reinforcement learning. Journal of Machine Learning Research.
Stulp, F., & Sigaud, O. (2012). Path integral policy improvement with covariance matrix adaptation. In Proceedings of the 29 International Conference on Machine Learning.
Bicchi, A. (1994). On the closure properties of robotic grasping. International Journal of Robotics Research, 14(4), 319–334.
Acknowledgements
This research has been partially funded by the EC Seventh Framework Programme (FP7) within RoDyMan project 320992 and by the national grant MUSHA under Programma STAR linea 1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Monforte, M., Ficuciello, F., Siciliano, B. (2019). Human Cognition-Inspired Robotic Grasping. In: Aldinhas Ferreira, M., Silva Sequeira, J., Ventura, R. (eds) Cognitive Architectures. Intelligent Systems, Control and Automation: Science and Engineering, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-97550-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-97550-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97549-8
Online ISBN: 978-3-319-97550-4
eBook Packages: EngineeringEngineering (R0)