Generalization of human grasping for multi-fingered robot hands

Heni Ben Amor, Oliver Kroemer, Ulrich Hillenbrand, Gerhard Neumann, Jan Peters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Pages2043-2050
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal
Duration: Oct 7 2012Oct 12 2012

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
Country/TerritoryPortugal
CityVilamoura, Algarve
Period10/7/1210/12/12

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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