Human prehension and dexterous robot hands
International Journal of Robotics Research
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Upper-Limb posture definition during grasping with task and environment constraints
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Hi-index | 0.00 |
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.