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Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-5821-7435
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0001-6824-6443
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-3958-6179
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2018 (English)In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018, 2018Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

n this work we combine sampling-based motionplanning with reinforcement learning and generative modelingto solve non-prehensile rearrangement problems. Our algorithmexplores the composite configuration space of objects and robotas a search over robot actions, forward simulated in a physicsmodel. This search is guided by a generative model thatprovides robot states from which an object can be transportedtowards a desired state, and a learned policy that providescorresponding robot actions. As an efficient generative model,we apply Generative Adversarial Networks.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Rearrangement planning, Pushing, Generative Adversarial Models
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-264024OAI: oai:DiVA.org:kth-264024DiVA, id: diva2:1371716
Conference
Workshop on "Machine Learning in Robot Motion Planning", International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 5 2018
Note

QCR 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2020-01-31Bibliographically approved

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Stork, Johannes A.Hang, KaiyuKragic, Danica

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