Upper-limb posture definition during grasping with task and environment constraints

Abstract

The purpose of this study is to propose a new tool to define the posture of a complete upper-limb 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. Without few prior information on the adequate posture, the model is able to find a suitable solution. Simulation results are proposed and commented. This tool can find interesting applications in the frame of gesture definition and simulation. © Springer-Verlag Berlin Heidelberg 2006.