Details

Autor(en) / Beteiligte

• Ben Amor, Heni
• Kroemer, O.
• Hillenbrand, U.
• Neumann, G.
• Peters, J.

Titel

Generalization of human grasping for multi-fingered robot hands

Ist Teil von

• 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, p.2043-2050

Ort / Verlag

IEEE

Erscheinungsjahr

2012

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.