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Entropy 2019, 21(2), 154; https://doi.org/10.3390/e21020154

Sensorless Pose Determination using Randomized Action Sequences

1
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2
Department Of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
*
Author to whom correspondence should be addressed.
Received: 14 December 2018 / Revised: 18 January 2019 / Accepted: 4 February 2019 / Published: 6 February 2019
PDF [891 KB, uploaded 6 February 2019]

Abstract

This paper is a study of 2D manipulation without sensing and planning, by exploring the effects of unplanned randomized action sequences on 2D object pose uncertainty. Our approach follows the work of Erdmann and Mason’s sensorless reorienting of an object into a completely determined pose, regardless of its initial pose. While Erdmann and Mason proposed a method using Newtonian mechanics, this paper shows that under some circumstances, a long enough sequence of random actions will also converge toward a determined final pose of the object. This is verified through several simulation and real robot experiments where randomized action sequences are shown to reduce entropy of the object pose distribution. The effects of varying object shapes, action sequences, and surface friction are also explored.
Keywords: manipulation; probabilistic reasoning, automation; manufacturing and logistics manipulation; probabilistic reasoning, automation; manufacturing and logistics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mannam, P.; Volkov, A.V., Jr.; Paolini, R.; Chirikjian, G.; Mason, M.T. Sensorless Pose Determination using Randomized Action Sequences. Entropy 2019, 21, 154.

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