A cortical network for control of voluntary movements in a robot finger

Abstract

Neurobiological control systems have long been studied as possible inspiration for the construction of robotic controllers. In this paper, a control of voluntary joint movements using a cortical network within constraints from neurophysiology and motor psychophysics is presented. The neural controller is proposed to control desired joint trajectories of an anthropomorphic robot finger. Each joint is driven by an agonist–antagonist actuator pair with muscle-like properties. Dynamical neural network proposes functional roles for pre-central cortical cell types in the computation of a descending command to spinal alpha and gamma motor neurons. Neurons in anterior area 5 are proposed to compute the position of the link in question using corollary discharges from area 4 and feedback from muscles spindles. Neurons in posterior area 5 use the position perception signal and desired position signal to compute a desired joint movement direction. The network reinterprets the “neural population activity” to afford unified control of posture, movement and force production. In addition, it suggests how the brain may set automatic and volitional gating mechanisms to vary the balance of static and dynamic feedback information to guide the movement command and to compensate for external forces. The reliability of the cortical neural controller is demonstrated by experimental results, where control system exhibits key kinematic properties of human movement, dynamic compensation and includes asymmetric bell-shaped velocity profiles.