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Learning Complex Functional Manipula...

Edmonds, Mark Joseph.

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Learning Complex Functional Manipulations by Human Demonstration and Fluent Discovery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning Complex Functional Manipulations by Human Demonstration and Fluent Discovery./
Author:	Edmonds, Mark Joseph.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10285902
ISBN:	9781369826869

Learning Complex Functional Manipulations by Human Demonstration and Fluent Discovery.
Edmonds, Mark Joseph.

Learning Complex Functional Manipulations by Human Demonstration and Fluent Discovery. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 47 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--University of California, Los Angeles, 2017.

Learning complex robot manipulation policies for real-world objects is extremely challenging, and often requires significant tuning within controlled environments. In this thesis, we learn an And-Or graph-based model to execute tasks with highly variable structure and multiple stages, which are typically not suitable for most policy learning approaches. The model is learned from human demonstration using a tactile glove that measures both hand pose and contact forces. The tactile glove enables observation of visually latent changes in the scene, specifically the forces imposed to unlock safety mechanisms. From these observations a stochastic grammar model is learned to represent the compositional nature of the task sequence, and the compatibility of that sequence with the observed tactile feedback. We present a method for transferring this human-specific knowledge onto a robot platform, and demonstrate that the robot can perform successful manipulations of unseen objects with similar task structure.

ISBN: 9781369826869Subjects--Topical Terms:

523869
Computer science.

Learning Complex Functional Manipulations by Human Demonstration and Fluent Discovery.
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300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: Song-Chun Zhu.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2017.
520 $a Learning complex robot manipulation policies for real-world objects is extremely challenging, and often requires significant tuning within controlled environments. In this thesis, we learn an And-Or graph-based model to execute tasks with highly variable structure and multiple stages, which are typically not suitable for most policy learning approaches. The model is learned from human demonstration using a tactile glove that measures both hand pose and contact forces. The tactile glove enables observation of visually latent changes in the scene, specifically the forces imposed to unlock safety mechanisms. From these observations a stochastic grammar model is learned to represent the compositional nature of the task sequence, and the compatibility of that sequence with the observed tactile feedback. We present a method for transferring this human-specific knowledge onto a robot platform, and demonstrate that the robot can perform successful manipulations of unseen objects with similar task structure.
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