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Carnegie Mellon Robotics Institute

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Abstract

Grasping an object is usually only an intermediate goal for a robotic manipulator. To finish the task, the robot needs to know where the object is in its hand and what action to execute. This paper presents a general statistical framework to address these problems. Given a novel object, the robot learns a statistical model of grasp state conditioned on sensor values. The robot also builds a statistical model of the requirements of the task in terms of grasp state accuracy. Both of these models are constructed by offline experiments. The online process then grasps objects and chooses actions to maximize likelihood of success. This paper describes the framework in detail, and demonstrates its effectiveness experimentally in placing, dropping, and insertion tasks. To construct statistical models, the robot performed over 8000 grasp trials, and over 1000 trials each of placing, dropping and insertion.

Notes

Associated Center(s) / Consortia: Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Manipulation Lab

Associated Project(s): Simple Hands

Text Reference

Robert Paolini, Alberto Rodriguez, Siddhartha Srinivasa, and Matthew T. Mason, "A Data-Driven Statistical Framework for Post-Grasp Manipulation," International Symposium on Experimental Robotics (ISER 2012), June, 2012.

BibTeX Reference

@inproceedings{Paolini_2012_7242,
   author = "Robert Paolini and Alberto Rodriguez and Siddhartha Srinivasa and Matthew T. Mason",
   title = "A Data-Driven Statistical Framework for Post-Grasp Manipulation",
   booktitle = "International Symposium on Experimental Robotics (ISER 2012)",
   month = "June",
   year = "2012",
}

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