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Learning-Based Class-Agnostic Segmentation of Grasped Objects

Boerdijk, Wout (2019) Learning-Based Class-Agnostic Segmentation of Grasped Objects. Master's. DLR-Interner Bericht. DLR-IB-RM-OP-2019-153.

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Abstract

Autonomous robotic applications require the ability to acquire information about newly encountered objects. However, currently most methods are trained in an offline manner, and require a large amount of annotated data. To meet the respective demand, this thesis proposes two methods to automatically obtain binary labels of grasped objects. Inspired by human learning through interaction, any object instance can be labeled just by concurrent manual manipulation and observation. Specifically, two approaches are explored: First, the presence of a hand in an image is leveraged as a cue to guide a network for the task of object-in-hand segmentation. A diverse set of labeled object instances enables the model to learn an object-agnostic item-in-hand representation. Second, a hand or robot manipulator representation is learned by exploiting motion cues as optical flow. Given a static scene, a moving grasped object can then be differentiated from the robot or hand for the segmentation of the grasped object. Through extensive experimental evaluation, the effectiveness of both methods under realistic conditions is verified.

Item URL in elib:https://elib.dlr.de/130799/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Learning-Based Class-Agnostic Segmentation of Grasped Objects
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Boerdijk, WoutWout.Boerdijk (at) dlr.deUNSPECIFIED
Date:2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Class Agnostic Object Segmentation, Grasped Object Segmentation, Segmentation from Motion, Robotic Object Manipulation, Deep Learning, Optical Flow
Institution:Technical University of Munich
Department:Department of Informatics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Boerdijk, Wout
Deposited On:19 Nov 2019 11:28
Last Modified:19 Nov 2019 11:28

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