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A method to learn hand grasping posture from noisy sensing information

Published online by Cambridge University Press:  20 May 2004

P. Gorce  and
N. Rezzoug
P. Gorce
Affiliation:
LESP EA 31 62 Université de Toulon et du Var, Avenue de l'université, BP 132, 83957 La Garde (France) E-mail: [email protected]
N. Rezzoug
Affiliation:
LESP EA 31 62 Université de Toulon et du Var, Avenue de l'université, BP 132, 83957 La Garde (France) E-mail: [email protected]
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Abstract

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.

Keywords

GraspingNeural networksReinforcement learningNoise and uncertainty
Type
Research Article
Information
Robotica , Volume 22 , Issue 3 , June 2004 , pp. 309 - 318
Copyright
© 2004 Cambridge University Press

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