A multistage neural network architecture to learn hand grasping posture

In this work, we focus our interest on hand grasping posture definition from few knowledge. For that a multistage neural network architecture is proposed that implements a reinforcement learning scheme on real valued outputs. Simulations results show good learning of grasping postures of various types of objects, with different numbers of fingers involved and different contacts configurations.