A learning-free method for anthropomorphic grasping


This work deals with grasping using an anthropomorphic hand. The main idea is to easily compute a grasp for a robotic hand in the context of a given task. This paper describes a method that does not require learning. Starting from works in the neuroscience field on human hand postural synergies, we introduce a two-level algorithm that uses a mathematical model of relationships between muscles and degrees-of-freedom of the hand and a set of five parameters to define synergies between muscles according to some grasp properties taken from an existing taxonomy of grasps. The two-level architecture presented in this paper aims to provide the flexibility needed for working with a real robotic hand. This algorithm is validated both in simulation using Gazebo and on the Shadow Robot Hand.

DOI: 10.1109/IROS.2013.6696779

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@article{Flavign2013ALM, title={A learning-free method for anthropomorphic grasping}, author={David Flavign{\'e} and V{\'e}ronique Perdereau}, journal={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, year={2013}, pages={2985-2990} }