TR-A-0144 :1992.4.30

Masazumi KATAYAMA, Mitsuo KAWATO

Virtual Trajectory and Stiffness Ellipse During Multi-Joint Arm Movement Predicted by Neural Inverse Models

Abstract:Because of the long delays associated with neural feedback loops, feedforward control is essential for relatively fast movements. Two approaches explaining the feedforward control of voluntary movements have been proposed in computational neuroscience for motor control. One avoids explicitly computing the inverse dynamics problem, and the other solves the problem by using learned internal models of the motor systems. In the former approach, a virtual trajectory control hypothesis has been intensively studied. According to this hypothesis, the brain computes the virtual trajectory and does not need to worry about low-level control problems. If experimentally observed roughly straight hand trajectories can be produced from such simple virtual trajectories as the straight minimum-jerk trajectory, complicated computations associated with the inverse dynamics problem need not be addressed. Thus, trajectory planning and control can be very simply performed. This paper compares the computational complexity of planning the virtual trajectory with that of solving the inverse dynamics problem. Computer simulations are performed using stiffness values during movement measured by Bennett et al. (Bennett, 1991; Bennett, Hollerbach, Xu, Hunter, 1992). The virtual trajectories and stiffness ellipses are predicted by neural network models which solve the inverse dynamics problem. The shape and orientation of the stiffness ellipses predicted during posture maintenance are similar to those measured in human experiments. The stiffness ellipses during movements depended greatly on the orientation, amplitude, and speed of movements. The virtual trajectories were much more complex than the actual trajectories. This indicates that planning the virtual trajectory is as difficult as solving the inverse dynamics problem, at least for fast movements. Finally, we propose a computational framework to integrate the virtual trajectory control hypothesis and learning neural internal models.