Generalization of Human Grasping for Multi-Fingered Robot Hands

Heni Ben Amor, Oliver Kroemer, Ulrich Hillenbrand, Gerhard Neumann and Jan Peters
Conference Paper, International Conference on Intelligent Robots and Systems (IROS), January, 2012

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Multi-fingered robot grasping is a challenging problem that is difficult to tackle using hand-coded programs. In this paper we present an imitation learning approach for learning and generalizing grasping skills based on human demonstrations. To this end, we split the task of synthesizing a grasping motion into three parts: (1) learning efficient grasp representations from human demonstrations, (2) warping contact points onto new objects, and (3) optimizing and executing the reach-and-grasp movements. We learn low-dimensional latent grasp spaces for different grasp types, which form the basis for a novel extension to dynamic motor primitives. These latent-space dynamic motor primitives are used to synthesize entire reach-and-grasp movements. We evaluated our method on a real humanoid robot. The results of the experiment demonstrate the robustness and versatility of our approach.

author = {Heni Ben Amor and Oliver Kroemer and Ulrich Hillenbrand and Gerhard Neumann and Jan Peters},
title = {Generalization of Human Grasping for Multi-Fingered Robot Hands},
booktitle = {International Conference on Intelligent Robots and Systems (IROS)},
year = {2012},
month = {January},
} 2019-03-12T14:28:51-04:00