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Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions
Royal Institute of Technology, Stockholm, Sweden.
Royal Institute of Technology, Stockholm, Sweden.
Royal Institute of Technology, Stockholm, Sweden. (AASS)ORCID iD: 0000-0003-3958-6179
Yale University, New Haven, USA.
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2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

In this work we combine sampling-based motion planning with reinforcement learning and generative modeling to solve non-prehensile rearrangement problems. Our algorithm explores the composite configuration space of objects and robot as a search over robot actions, forward simulated in a physics model. This search is guided by a generative model that provides robot states from which an object can be transported towards a desired state, and a learned policy that provides corresponding robot actions. As an efficient generative model, we apply Generative Adversarial Networks.

Place, publisher, year, edition, pages
2018.
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-78675OAI: oai:DiVA.org:oru-78675DiVA, id: diva2:1379415
Conference
Workshop "Machine Learning in Robot Motion Planning" at International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, October 1-5, 2018
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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Stork, Johannes Andreas

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Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf