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Autonomous manipulation with a general-purpose simple hand.

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Bibliographic Details
Title: Autonomous manipulation with a general-purpose simple hand.
Authors: Mason, Matthew T1, Rodriguez, Alberto1, Srinivasa, Siddhartha S1, Vazquez, Andrés S2
Source: International Journal of Robotics Research. May2012, Vol. 31 Issue 5, p688-703. 16p.
Subjects: Robotics, Table manipulation (Computer science), Machine theory, Prototypes, Localization theory, Object recognition (Computer vision), Computer vision
Abstract: While complex hands seem to offer generality, simple hands are often more practical. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands, it considers several familiar examples, and it proposes an approach for autonomous manipulation using a general-purpose but simple hand. We explore the approach in the context of a bin-picking task, focused on grasping, recognition, and localization. The central idea is to use learned knowledge of stable grasp poses as a cue for object recognition and localization. This leads to some novel design criteria, such as minimizing the number of stable grasp poses. Finally, we describe experiments with two prototype hands to perform bin-picking of highlighter markers. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
Description
Abstract:While complex hands seem to offer generality, simple hands are often more practical. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands, it considers several familiar examples, and it proposes an approach for autonomous manipulation using a general-purpose but simple hand. We explore the approach in the context of a bin-picking task, focused on grasping, recognition, and localization. The central idea is to use learned knowledge of stable grasp poses as a cue for object recognition and localization. This leads to some novel design criteria, such as minimizing the number of stable grasp poses. Finally, we describe experiments with two prototype hands to perform bin-picking of highlighter markers. [ABSTRACT FROM PUBLISHER]
ISSN:02783649
DOI:10.1177/0278364911429978