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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17026

Title: Non-Prehensile Manipulation Learning through Self-Supervision
Authors: Gao, Ziyan
Elibol, Armagan
Chong, Nak Young
Keywords: non-prehensile manipulation
state representation learning
fully convolutional autoencoder
mixture density network
Issue Date: 2020-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Start page: 93
End page: 99
DOI: 10.1109/IRC.2020.00022
Abstract: Manipulation is one of most emerging research and development areas in the field of robotics. Recently, state representation learning for control has been gaining attention. In this paper, we proposed a novel learning model based on neural networks in order to sample the actions of the robot to push objects to desired positions. Furthermore, an intuitive method was proposed to enable the robot to collect training data in an efficiently way. Specifically, a fully convolutional network encodes observations into latent space, and a mixture density network is implemented to infer an action distribution, since there are an infinite number of possible actions that may result in the same change of the state of the object. Through extensive experimental simulations and comparisons with the existing models, we demonstrated the efficiency of the proposed method applied to non-prehensile manipulation, such as pushing or rotating of small objects on the table.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020, pp.93-99. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/17026
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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