Abort and Retry in Grasping

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

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

author = {Alberto Rodriguez and Matthew T. Mason and Siddhartha Srinivasa and Matthew Bernstein and Alex Zirbel},
title = {Abort and Retry in Grasping},
booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS 2011)},
year = {2011},
month = {September},
} 2017-09-13T10:40:09-04:00