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Robots that Anticipate Pain : = Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Robots that Anticipate Pain :/
Reminder of title:	Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models.
Author:	Sur, Indranil.
Description:	1 online resource (49 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-04.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9781369743524

Robots that Anticipate Pain : = Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models.
Sur, Indranil.

Robots that Anticipate Pain :Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models. - 1 online resource (49 pages)

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

Thesis (M.S.)--Arizona State University, 2017.

Includes bibliographical references

To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple subnetworks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369743524Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

Robots that Anticipate Pain : = Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models.
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500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: Heni B. Amor.
502 $a Thesis (M.S.)--Arizona State University, 2017.
504 $a Includes bibliographical references
520 $a To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple subnetworks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 561941
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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