Autonomous Robots

, Volume 31, Issue 1, pp 1–20

Data-driven grasping

Authors

    • Columbia University
  • Peter K. Allen
    • Columbia University
Article

DOI: 10.1007/s10514-011-9228-1

Cite this article as:
Goldfeder, C. & Allen, P.K. Auton Robot (2011) 31: 1. doi:10.1007/s10514-011-9228-1

Abstract

This paper propose a novel framework for a data driven grasp planner that indexes partial sensor data into a database of 3D models with known grasps and transfers grasps from those models to novel objects. We show how to construct such a database and also demonstrate multiple methods for matching into it, aligning the matched models with the known sensor data of the object to be grasped, and selecting an appropriate grasp to use. Our approach is experimentally validated in both simulated trials and trials with robots.

Keywords

Grasping Robotics Data-driven

Supplementary material

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Copyright information

© Springer Science+Business Media, LLC 2011