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Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
HKUST Robotics Institute, Hong Kong University of Science and Technology, Hong Kong.
Robotics, Perception and Learning Lab, Centre for Autonomous Systems, KTH Royal Institute of Technology, Stockholm, Sweden. (AASS)ORCID iD: 0000-0003-3958-6179
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2018 (English)In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2018, p. 270-277Conference paper, Published paper (Refereed)
Abstract [en]

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2018. p. 270-277
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-71554DOI: 10.1109/ICRA.2018.8462863ISI: 000446394500028OAI: oai:DiVA.org:oru-71554DiVA, id: diva2:1280224
Conference
IEEE International Conference on Robotics and Automation, Brisbane, QLD, Australia, May 21-25, 2018
Funder
Knut and Alice Wallenberg Foundation
Note

Funding agency: Hong Kong University of Science and Technology

Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-01-22Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf