Using Vision for Pre- and Post-grasping Object Localization for Soft Hands

Changhyun Choi, Joseph Del Preto, Daniela Rus

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we present soft hands guided by an RGB-D object perception algorithm which is capable of localizing the pose of an object before and after grasping. The soft hands can perform manipulation operations such as grasping and connecting two parts. The flexible soft grippers grasp objects reliably under high uncertainty but the poses of the objects after grasping are subject to high uncertainty. Visual sensing ameliorates the increased uncertainty by means of in-hand object localization. The combination of soft hands and visual object perception enables our Baxter robot, augmented with soft hands, to perform object assembly tasks which require high precision. The effectiveness of our approach is validated by comparing it to the Baxter’s original hard hands with and without the in-hand object localization.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Pages601-612
Number of pages12
DOIs
StatePublished - 2017

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume1
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by The Boeing Company. The support is gratefully acknowledged.

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

  • In-hand object localization
  • Pose estimation
  • Robotic assembly
  • Soft gripper
  • Soft hands
  • Vision-guided manipulation

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