Entropy 2019, 21(2), 154; https://doi.org/10.3390/e21020154
Sensorless Pose Determination using Randomized Action Sequences
Pragna Mannam 1,*
, Alexander Volkov Volkov, Jr. 1, Robert Paolini 1, Gregory Chirikjian 2 and Matthew T. Mason 1
, Alexander Volkov Volkov, Jr. 1, Robert Paolini 1, Gregory Chirikjian 2 and Matthew T. Mason 1
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
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Recent Advances in Entropy and Information-Theoretic Concepts and Their Applications)
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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.
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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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