Abstract
Recent work on the analysis of natural and robotic hands has introduced the notion of postural synergies as a principled organization of their complexity, based on the physical characteristics of the hand itself. Such characteristics include the mechanical arrangements of joints and fingers, their couplings, and the low-level control reflexes, that determine the specific way the concept of “hand” is embodied in a human being or a robot. While the focus of work done so far with postural synergies has been on motion planning for grasp acquisition, in this paper we set out to investigate the role that different embodiments have on the choice of grasping forces, and on the ultimate quality of the grasp. Numerical results are presented showing quantitatively the role played by different synergies (from the most fundamental to those of higher-order) in making a number of different grasps possible. The effect of number and types of engaged synergies on the distribution of optimal grasp forces is considered. Moreover, robustness of results is investigated with respect to variation in uncertain parameters such as contact and joint stiffness.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bicchi, A. (1994). On the problem of decomposing grasp and manipulation forces in multiple whole-limb manipulation. Robotics and Autonomous Systems, 13(2), 127–147.
Bicchi, A. (1995). On the closure properties of robotic grasping. The International Journal of Robotics Research, 14(4), 319–334.
Bicchi, A. (2000). Hands for dextrous manipulation and robust grasping: a difficult road towards simplicity. IEEE Transactions on Robotics and Automation, 16(6), 652–662.
Bicchi, A., & Prattichizzo, D. (2000). Analysis and optimization of tendinous actuation for biomorphically designed robotic systems. Robotica, 18(1), 23–31.
Borgstrom, H., Batalin, M., Sukhatme, G., & Kaiser, W. (2010). Weighted barrier functions for computation of force distributions with friction cone constraints. In 2010 IEEE ICRA, Anchorage, Alaska, USA (pp. 785–792).
Brown, C., & Asada, H. (2007). Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal component analysis. In IEEE-RAS international conference on intelligent robots and systems (pp. 2877–2882).
Buss, M., Hashimoto, H., & Moore, J. B. (1996). Dextrous hand grasping force optimization. IEEE Transactions on Robotics and Automation, 12(3), 406–418.
Chen, S. F. (2000). Conservative congruence transformation for joint and cartesian stiffness matrices of robotic hands and fingers. The International Journal of Robotics Research, 19(9), 835–847.
Cheung, V., d’Avella, A., Tresch, M., & Bizzi, E. (2005). Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors. The Journal of Neuroscience, 25(27), 6419–6434.
Ciocarlie, M. T., & Allen, P. K. (2009). Hand posture subspaces for dexterous robotic grasping. The International Journal of Robotics Research, 28(7), 851–867.
Ciocarlie, M., Goldfeder, C., & Allen, P. (2007). Dimensionality reduction for hand-independent dexterous robotic grasping. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 3270–3275).
Cutkosky, M., & Kao, I. (1989). Computing and controlling the compliance of a robotic hand. IEEE Transactions on Robotics and Automation, 5(2), 151–165.
Featherstone, R. (2008). Rigid body dynamics algorithms. Berlin: Springer.
Feldman, A. G., & Levin, M. F. (2009). The equilibrium-point hypothesis–past, present and future. Advances in Experimental Medicine and Biology, 629, 699–726.
Friedman, J., & Flash, T. (2007). Task-dependent selection of grasp kinematics and stiffness in human object manipulation. Cortex, 43, 444–460.
Grant, M., & Boyd, S. (2004). Cvx: Matlab software for disciplined convex programming (web page and software). http://stanford.edu/boyd/cvx.
Grant, M., & Boyd, S. (2008). Graph implementations for nonsmooth convex programs. In Recent advances in learning and control (pp. 95–110).
Hanafusa, H., & Asada, H. (1997). Stable prehension by a robot hand with elastic fingers. In 7th ISIR, Tokyo.
Joh, J., & Lipkin, H. (1991). Lagrangian wrench distribution for cooperating robotic mechanisms. In IEEE conf. on robotics and automation.
Kao, I., Cutkosky, M., & Johansson, R. (1997). Robotic stiffness control and calibration as applied to human grasping tasks. IEEE Transactions on Robotics and Automation, 13(4), 557–566.
Kim, K., Youm, Y., & Chung, W. (2002). Human kinematic factor for haptic manipulation: the wrist to thumb. In Proceedings. 10th symposium on Haptic interfaces for virtual environment and teleoperator systems, 2002. HAPTICS 2002 (pp. 319–326).
Lee, J., & Kunii, T. (1995). Model-based analysis of hand posture. IEEE Computer Graphics and Applications, 15(5), 77–86.
Lin, J., & Wu, T. (2000). Modeling the constraints of human hand motion. Urbana, 51(61), 801.
Linscheid, R., An, K., & Gross, R. (1991). Quantitative analysis of the intrinsic muscles of the hand. Clinical Anatomy, 4(4), 265–284.
Mason, C.R., Gomez, J. E., & Ebner, T. J. (2001). Hand synergies during reach-to-grasp. Journal of Neurophysiology, 86(6), 2896–2910.
Murray, R. M., Li, Z., & Sastry, S. (1994). A mathematical introduction to robotic manipulation. Boca Raton: CRC Press.
Prattichizzo, D., & Trinkle, J. C. (2000). Grasping. In Springer Handbook of Robotics. Berlin: Springer.
Santello, M., Flanders, M., & Soechting, J. F. (1998). Postural hand synergies for tool use. The Journal of Neuroscience, 18(23), 10105–10115.
Toh, K. C., Todd, M., & Tutuncu, R. (1999). Sdpt3—a matlab software package for semidefinite programming. Optimization Methods & Software, 1, 545–581.
Youm, Y., Holden, M., & Dohrmann, K. (1977). Finger ray ratio study (Technical Report on Wrist Project).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gabiccini, M., Bicchi, A., Prattichizzo, D. et al. On the role of hand synergies in the optimal choice of grasping forces. Auton Robot 31, 235–252 (2011). https://doi.org/10.1007/s10514-011-9244-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-011-9244-1