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8 octobre 2006
- handle: 10670/1.eny9r2
- archivesic: sic_01798002
- doi: 10.1109/ICSMC.2006.384851
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info:eu-repo/semantics/altIdentifier/doi/10.1109/ICSMC.2006.384851
Mots-clés
Learning Anthropomorphism Fingers Neural networks Kinematics Cybernetics Shape Orbital robotics Ergonomics BiomechanicsSujets proches
Nurture and nature Nature versus nurture Genetics and environment Nature--Nurture Environment Heredity and environment Pattern Model Environment Ecological science Environmental biology Bionomics Ecological sciences Environment Balance of nature Oecology Biology--Ecology Ecological processes EnvironmentCiter ce document
Nasser Rezzoug et al., « Learning to grasp in unknown environment by reinforcement learning and shaping », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1109/ICSMC.2006.384851
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Résumé
The purpose of this study is to propose a new tool to define the posture of a complete anthropomorphic arm model during grasping taking into account task and environment constraints. The developed model is based on a neural network architecture mixing both supervised and reinforcement learning. The task constraints are materialized by target points to be reached by the fingertips on the surface of the object to be grasped while environment constraints are represented by obstacles. With no prior information on the shape, position and number of obstacles, the model is able to find a suitable solution according to specified criteria. Simulation results are proposed and commented.