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In this paper, we propose a new method to learn a multi-fingered hand grasping posture with little knowledge about the task and few sensing capabilities. The developed model is composed of two stages. The first is dedicated to the finger inverse kinematics learning in order to provide the fingertip-desired position. This function is fulfilled by modular neural network architecture. Following the concept of reinforcement learning, a second neural model dealing with noisy sensing information is used to search the space of hand configuration. Simulation results show a good learning of grasping postures with five fingers and different noise levels.
The goal of this paper is to present a new method to control pushing operations with several fingers. We take into account some dynamical aspects that have not yet been investigated in pushing studies such as the object's center of mass acceleration correction and optimal force distribution. To do this, we use a general method for multi-chain mechanisms based on a dynamical control with finger coordination. We first detail the problems encountered in this type of manipulation and the way to take these points into account with specific constraints definition for the support contact. According to the parameters of the model such as optimization criterion and object characteristics, we
present simulation results of object pushing with two fingers for a rotational and translational motion.
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