Reactive grasping using optical proximity sensors

Authors:
Kaijen Hsiao;Paul Nangeroni;Manfred Huber;Ashutosh Saxena;Andrew Y. Ng
Affiliations:
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Department of Mechanical Engineering, Stanford University, Stanford, CA;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX;Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

We propose a system for improving grasping using fingertip optical proximity sensors that allows us to perform online grasp adjustments to an initial grasp point without requiring premature object contact or regrasping strategies. We present novel optical proximity sensors that fit inside the fingertips of a Barrett Hand, and demonstrate their use alongside a probabilistic model for robustly combining sensor readings and a hierarchical reactive controller for improving grasps online. This system can be used to complement existing grasp planning algorithms, or be used in more interactive settings where a human indicates the location of objects. Finally, we perform a series of experiments using a Barrett hand equipped with our sensors to grasp a variety of common objects with mixed geometries and surface textures.