Kinematic Feedback Control Laws for generating Natural Arm Movements

[ Information ]

Bioinspiration & Biomimetics, vol. 9, no. 1, p. 016002, 2013.

[ Authors ]

Donghyun Kim, Cheongjae Jang, and Frank C. Park

[ Abstract ]

We propose a stochastic optimal feedback control law for generating natural robot arm motions. Our approach, inspired by the minimum variance principle of Harris and Wolpert (1998 Nature 394 780–4) and the optimal feedback control principles put forth by Todorov and Jordan (2002 Nature Neurosci. 5 1226–35) for explaining human movements, differs in two crucial respects: (i) the endpoint variance is minimized in joint space rather than Cartesian hand space, and (ii) we ignore the dynamics and instead consider only the second-order differential kinematics. The feedback control law generating the motions can be straightforwardly obtained by backward integration of a set of ordinary differential equations; these equations are obtained exactly, without any linear–quadratic approximations. The only parameters to be determined a priori are the variance scale factors, and for both the two-DOF planar arm and the seven-DOF spatial arm, a table of values is constructed based on the given initial and final arm configurations; these values are determined via an optimal fitting procedure, and consistent with existing findings about neuromuscular motor noise levels of human arm muscles. Experiments conducted with a two-link planar arm and a seven-DOF spatial arm verify that the trajectories generated by our feedback control law closely resemble human arm motions, in the sense of producing nearly straight-line hand trajectories, having bell-shaped velocity profiles, and satisfying Fitts Law.