Yasuhiro WADA, Mitsuo KAWATO
A Neural Network Model
for Arm Trajectory Formation
Using Forward and Inverse
Dynamics Models
Abstract:The minimum torque-change model predicts and reproduces human multi-joint movement
data quite well. However, there are three criticisms of the current neural network models
for trajectory formation based on the minimum torque-change criterion: (1) their spatial
representation of time, (2) backpropagation is essential, and (3) they require too many
iterations. Accordingly, we propose a new neural network model for trajectory formation
based on the minimum torque-change criterion. Our neural network model basically uses
a forward dynamics model, an inverse dynamics model and a trajectory. formation
mechanism which generates an approximate minimum torque-change trajectory. It does
not require spatial representation of time or backpropagation. Furthermore, there are less
iterations required to obtain an approximate optimal solution. Finally, our neural network
model can be broadly applied to the engineering field because it is a new method for
solving optimization problems with boundary conditions.