Eiichi Inohira

Improvement of a neural network-based motion generator with bimanual coordination for upper limb prosthesis

We propose to redesign a neural network used as a motion generator with bimanual coordination for upper limb prosthesis in order to improve its learning capability. We assumed that the wearer of the prosthesis was a unilateral amputee. In our previous work, we proposed a prosthesis control system using a neural network that learned bimanual coordination in order to implement smooth motion with both hands. However, the previously proposed system has the problem that a neural network cannot generate the desired motion of the prosthesis in special cases. The reason is that the motion generator calculates the desired posture of the prosthesis from the current posture of the healthy arm only, regardless of the current posture of the prosthesis. We propose to use the current posture of both the healthy arm and the prosthesis as neural network inputs in order to solve this problem. In this article, we show that a single neural network whose input was the current posture of both arms could learn the relationships of the coordinated motions of holding boxes of different sizes, and the newly proposed system can calculate the desired motion of the prosthesis in special cases through computer simulations.

Evaluation of an optimal design method for a multilayer perceptron by using the design of experiments

We evaluated the performance of an optimal design method for a multilayer perceptron (MLP) by using the design of experiments (DOE). In our previous work, we proposed an optimal design method for MLPs in order to determine the optimal values of such parameters as the number of neurons in the hidden layers and the learning rates. In this article, we evaluate the performance of the proposed design method through a comparison with a genetic algorithm (GA)-based design method. We target an optimal design of MLPs with six layers. We also evaluate the proposed designed method in terms of calculating the amount of optimization. Through the above-mentioned evaluation and analysis, we aim at improving the proposed design method in order to obtain an optimal MLP with less effort.

A neural network based hierarchical motor schema of a multi-finger hand and its motion diversity

This paper presents a neural network based hierarchical motor schema of a multi finger hand to generate suitable behavior for an unknown situation without retraining all neural networks and investigates its motion diversity by changing its input signals. Conventional neural networks are hard to generate desired movements in an unknown situation. Our hierarchical motor schema consists of the two layers. A lower schema is implemented by a recurrent neural network trained with primitive movement patterns and generates a finger movement from a command code sent from the upper schema. The upper schema generates command codes to each finger from a behavior command code such as grasping. We showed that though the lower schemata were fixed, diversity of generated finger movements can be obtained by changing a behavior code of the upper schema through computer simulation.

Perturbational Neural Networks for Incremental Learning in Virtual Learning System

This paper presents a new type of neural networks, a perturbational neural network to realize incremental learning in autonomous humanoid robots. In our previous work, a virtual learning system has been provided to realize exploring plausible behavior in a robots brain. Neural networks can generate plausible behavior in unknown environment without time-consuming exploring. Although an autonomous robot should grow step by step, conventional neural networks forget prior learning by training with new dataset. Proposed neural networks features adding output in sub neural network to weights and thresholds in main neural network. Incremental learning and high generalization capability are realized by slightly changing a mapping of the main neural network. We showed that the proposed neural networks realize incremental learning without forgetting through numerical experiments with a two-dimensional stair-climbing bipedal robot.