Generalization of human grasping for multi-fingered robot hands

Heni Ben Amor, Oliver Kroemer, Ulrich Hillenbrand, Gerhard Neumann, Jan Peters
2012 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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
more » ... ments. 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.
doi:10.1109/iros.2012.6386072 dblp:conf/iros/AmorKHNP12 fatcat:fx4fewtjejgwbf2tso5k2lqrsa