Abstract

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.