Grasping posture learning with noisy sensing information for a large scale of multifingered robotic systems: Research Articles

In this paper, we propose a new method to learn multifingered hand configuration during grasping in the presence of noise and uncertainty. The developed model is composed of two modules. The first one carries out the learning of the fingers inverse kinematics. It is based on a modular architecture composed of several neural networks. Using reinforcement learning, a second neural network based model optimizes the position and orientation of the hand palm taking into account noisy sensing information. Working together these two modules exchange information to define the complete hand configuration. In order to illustrate the capabilities of the proposed model, simulation results are proposed using different kinds of objects, different levels of noise, and for a multifingered hand with different number of fingers. © 2005 Wiley Periodicals, Inc.