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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/11412

Title: Adaptive Point-Based Value Iteration for Continuous States POMDP in Goal-Directed Imitation Learning
Authors: Pratama, Ferdian Adi
Lee, Hosun
Lee, Geunho
Chong, Nak Young
Keywords: Imitation
Sequential Decision Making
POMDP
Motion Planning
Issue Date: 2012-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)
Start page: 249
End page: 254
DOI: 10.1109/URAI.2012.6462987
Abstract: In motion planning and robot navigation, continuous domain would be the natural way of representation of state space. However, discretization is needed in order to deal with continuous state space. Results precision depends on the discretization, which leads to a problem of ”curse of dimensionality”. We present a new approximation approach of goal-directed imitation learning algorithm using the point-based value iteration algorithm that deals with continuous domain in motion planning. We demonstrate our algorithm in the V-REP robot simulator, to validate the experimental result.
Rights: This is the author's version of the work. Copyright (C) 2012 IEEE. 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2012, 249-254. 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/10119/11412
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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