Extrinsic dexterity: In-hand manipulation with external forces
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Rodriguez_Extrinsic dexterity.pdf
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Author(s)
Paolini, Robert
Tang, Bowei
Srinivasa, Siddhartha S.
Mason, Matthew T.
Lundberg, Ivan
Staab, Harald
Fuhlbrigge, Thomas
Chavan Dafle, Nikhil Narsingh
Rodriguez Garcia, Alberto
Erdmann, Michael A.
Date Issued
June 2014
Journal
2014 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dafle, Nikhil Chavan, Alberto Rodriguez, Robert Paolini, Bowei Tang, Siddhartha S. Srinivasa, Michael Erdmann, Matthew T. Mason, Ivan Lundberg, Harald Staab, and Thomas Fuhlbrigge. “Extrinsic Dexterity: In-Hand Manipulation with External Forces.” 2014 IEEE International Conference on Robotics and Automation (ICRA) (May 2014).
Version
Author's final manuscript
Abstract
“In-hand manipulation” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and hand-scripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2014.6907062
