A NEURAL-NETWORK MODEL FOR ARM TRAJECTORY FORMATION USING FORWARD AND INVERSE DYNAMICS MODELS

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 criteria: (1) their spatial representation of time, (2) back propagation 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 back propagation. 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.