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Abort and Retry in Grasping

Alberto Rodriguez, Matthew T. Mason, Siddhartha Srinivasa, Matthew Bernstein and Alex Zirbel
Conference Paper, Carnegie Mellon University, IEEE International Conference on Intelligent Robots and Systems (IROS 2011), September, 2011

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Iteration is often sufficient for a simple hand to accomplish complex tasks, at the cost of an increase in the expected time to completion. In this paper, we minimize that overhead time by allowing a simple hand to abort early and retry as soon as it realizes that the task is likely to fail. We present two key contributions. First, we learn a probabilistic model of the relationship between the likelihood of success of a grasp and its grasp signature–the trace of the state of the hand along the entire grasp motion. Second, we model the iterative process of early abort and retry as a Markov chain and optimize the expected time to completion of the grasping task by effectively thresholding the likelihood of success. Experiments with our simple hand prototype tasked with grasping and singulating parts from a bin show that early abort and retry significantly increases efficiency.

BibTeX Reference
title = {Abort and Retry in Grasping},
author = {Alberto Rodriguez and Matthew T. Mason and Siddhartha Srinivasa and Matthew Bernstein and Alex Zirbel},
booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS 2011)},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2011},
address = {Pittsburgh, PA},