The role of the RBF training in a neural model for object grasping

Presents a neural system to determine three contact points between a gripper and an object of arbitrary shape. The neural system is composed of three functional blocks to capture and process the image, establish the contact points and estimate the contact forces. The second block is formed by two neural networks. The first network (competitive Hopfield neural network) determines an approximate polygon for an object outline. A second network, a RBF or MLP model, defines three contact points. The results suggest that the neural system always reaches stable grasping for known and unknown objects Moreover, the training methods used by the RBF model influences significantly the performance and the learning speed of the system