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Affordance Detection for Task-Specific Grasping Using Deep Learning
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
2017 (English)In: 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 91-98Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 91-98
Series
IEEE-RAS International Conference on Humanoid Robots, ISSN 2164-0572
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225257DOI: 10.1109/HUMANOIDS.2017.8239542ISI: 000427350100013Scopus ID: 2-s2.0-85044473077ISBN: 9781538646786 (print)OAI: oai:DiVA.org:kth-225257DiVA, id: diva2:1194484
Conference
2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS)
Funder
Wallenberg FoundationsSwedish Foundation for Strategic Research Swedish Research Council
Note

QC 20180403

Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-04-06Bibliographically approved

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Stork, Johannes A.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Output format
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