Page 87 - Human Inspired Dexterity in Robotic Manipulation

Modeling and Human Performance in Manipulating Parallel Flexible Objects 83

[14] N. Hogan, An organizing principle for a class of voluntary movements, J. Neurosci.
4 (11) (1984) 2745–2754.
[15] J. Dingwell, C. Mah, F. Mussa-Ivaldi, Experimentally confirmed mathematical model
for human control of a non-rigid object, J. Neurophysiol. 91 (2004) 1158–1170.
[16] M. Svinin, I. Goncharenko, Z. Luo, S. Hosoe, Reaching movements in dynamic envi-
ronments: how do we move flexible objects? IEEE Trans. Robot. 22 (4) (2006)
724–739.
[17] R. Leib, A. Karniel, Minimum acceleration with constraints of center of mass: a unified
model for arm movements and object manipulation, J. Neurophysiol. 108 (6) (2012)
1646–1655.
[18] C. Van Loan, Computing integrals involving the matrix exponential, IEEE Trans.
Autom. Control 23 (3) (1978) 395–404.
[19] M. Svinin, Y. Masui, Z. Luo, S. Hosoe, On the dynamic version of the minimum hand
jerk criterion, J. Robot. Syst. 22 (11) (2005) 661–676.
[20] K. Arthurs, D. Andrews, Upper extremity soft and rigid tissue mass prediction using
segment anthropometric measures and DXA, J. Biomech. 42 (3) (2009) 389–394.
[21] T. Tsuji, P. Morasso, K. Goto, K. Ito, Human hand impedance characteristics during
maintained posture in multi-joint arm movements, Biol. Cybern. 72 (1995) 475–485.
[22] J. Speich, L. Shao, M. Goldfarb, Modeling the human hand as it interacts with a tele-
manipulation system, Mechatronics 15 (2005) 1127–1142.
[23] P. Marayong, G. Hager, A. Okamura, Effect of hand dynamics on virtual fixtures for
compliant human-machine interfaces, in: 14th International Symposium on Haptic
Interfaces for Virtual Environment and Teleoperator Systems, Alexandria, VA, 2006,
pp. 109–115.
¸
[24] M. Fu, M. Cavuşog ˘lu, Human-arm-and-hand-dynamic model with variability analyses
for a stylus-based haptic interface, IEEE Trans. Syst. Man Cybern. B 42 (6) (2012)
1633–1644.
[25] D. Wolpert, Z. Ghahramani, M. Jordan, Are arm trajectories planned in kinematic or
dynamic coordinate? An adaptation study, Exp. Brain Res. 103 (1995) 460–470.
[26] M. Kawato, Trajectory formation in arm movements: minimization principles and pro-
cedures, in: H. Zelaznik (Ed.), Advances in Motor Learning and Control, Human
Kinetics Publishers, Champaign, IL, 1996, pp. 225–259.
[27] I. Goncharenko, M. Svinin, S. Forstmann, Y. Kanou, S. Hosoe, On the influence of
arm inertia and configuration on motion planning of reaching movements in haptic
environments, in: World Haptics 2007, 2nd Joint Eurohaptic Conference & 15th IEEE
Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems,
Tsukuba, Japan, 2007, pp. 33–38.
[28] C. Harris, D. Wolpert, Signal-dependent noise determines motor planning, Nature
394 (1998) 780–784.
[29] C. Harris, On the optimal control of behavior: a stochastic perspective, J. Neurosci.
Methods 83 (1998) 73–88.
[30] M. Svinin, M. Yamamoto, A mathematical analysis of the minimum variance model of
human-like reaching movements, in: Proceedings of IEEE/RSJ International Confer-
ence on Intelligent Robots and Systems, IROS’2011, San Francisco, CAvol. 4, 2011.
[31] M. Svinin, I. Goncharenko, S. Hosoe, On the boundary conditions in modeling of
human-like reaching movements, in: Proceedings of IEEE/RSJ International Confer-
ence on Intelligent Robots and Systems, IROS’2008, Nice, Francevol.1, 2008.
[32] S. Ben-Itzhak, A. Karniel, Minimum acceleration criterion with constraints implies
bang-bang control as an underlying principle for optimal trajectories of arm reaching
movements, Neural Comput. 20 (3) (2008) 779–812.
[33] R. Happee, Time optimality in the control of human movements, Biol. Cybern. 66 (4)
(1992) 357–366.