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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17574

Title: Planar Pushing of Unknown Objects Using a Large-Scale Simulation Dataset and Few-Shot Learning
Authors: Gao, Ziyan
ELIBOL, Armagan
Nak-Young, Chong
Issue Date: 2021-08
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
Start page: 341
End page: 347
DOI: 10.1109/CASE49439.2021.9551513
Abstract: Contact-rich object manipulation skills challenge the recent success of learning-based methods. It is even more difficult to predict the state of motion of novel objects due to the unknown physical properties and generalization issues of the learning-based model. In this work, we aim to predict the dynamics of novel objects in order to facilitate model-based control methods in planar pushing. We deal with this problem in two aspects. First, we present a large-scale planar pushing simulation dataset called SimPush. It is characterized by a large number of pushes and a variety of object physical properties, providing a wide avenue for exploring the object responses to the pusher action. Secondly, we propose a novel task-aware representation for pushes. This method keeps the spatial relation between the object and pusher and emphasizes the local contact features. Finally, we propose an encoder-decoder structured model possessing a cascaded residual attention mechanism to integrate prior knowledge to infer novel object motions. We experimentally show that the proposed model purely trained by SimPush attains good performance and robust prediction of novel object motions.
Rights: This is the author's version of the work. Copyright (C) 2021 IEEE. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021, 341-347. 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/10119/17574
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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