Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware

Charles C. Phiri; Zhaojie Ju; Naoyuki Kubota; Honghai Liu

doi:10.1109/MHS.2016.7824246

Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware

Charles C. Phiri, Zhaojie Ju, Naoyuki Kubota, Honghai Liu

Tokyo Metropolitan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

In this paper we continue with previous work by the authors implementing context-aware middleware to accelerate robot learning from demonstration, LfD. Specifically, we apply Fuzzy Q-Learning, FQL, reinforcement learning strategy to enhance the learning experience of the robot. Typically, fuzzy techniques allow the robot to make decisions without the need for an exhaustive map of the world. FQL, approximates the observable configuration space allowing the robot to overcome the high dimension challenge of feature decomposition and navigation in a stochastic environment.

Original language	English
Title of host publication	2016 International Symposium on Micro-NanoMechatronics and Human Science (MHS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1-5090-2785-9
ISBN (Print)	978-1-5090-2786-6
DOIs	https://doi.org/10.1109/MHS.2016.7824246
Publication status	Published - 19 Jan 2017
Event	27th International Symposium on Micro-NanoMechatronics and Human Science: MHS 2016 - Nagoya, Japan Duration: 28 Nov 2016 → 30 Nov 2016 http://www.mein.nagoya-u.ac.jp/MHS/mhs2016-Top.html

Publication series

Name
ISSN (Electronic)	2474-3771

Conference

Conference	27th International Symposium on Micro-NanoMechatronics and Human Science
Country/Territory	Japan
City	Nagoya
Period	28/11/16 → 30/11/16
Internet address	http://www.mein.nagoya-u.ac.jp/MHS/mhs2016-Top.html

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10.1109/MHS.2016.7824246

Enhanced Robot Learning using Fuzzy Q-Learning & Context-Aware
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http://ieeexplore.ieee.org/document/7824246/

Cite this

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title = "Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware",

abstract = "In this paper we continue with previous work by the authors implementing context-aware middleware to accelerate robot learning from demonstration, LfD. Specifically, we apply Fuzzy Q-Learning, FQL, reinforcement learning strategy to enhance the learning experience of the robot. Typically, fuzzy techniques allow the robot to make decisions without the need for an exhaustive map of the world. FQL, approximates the observable configuration space allowing the robot to overcome the high dimension challenge of feature decomposition and navigation in a stochastic environment.",

author = "Phiri, {Charles C.} and Zhaojie Ju and Naoyuki Kubota and Honghai Liu",

year = "2017",

month = jan,

day = "19",

doi = "10.1109/MHS.2016.7824246",

language = "English",

isbn = "978-1-5090-2786-6",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2016 International Symposium on Micro-NanoMechatronics and Human Science (MHS)",

address = "United States",

note = "27th International Symposium on Micro-NanoMechatronics and Human Science : MHS 2016 ; Conference date: 28-11-2016 Through 30-11-2016",

url = "http://www.mein.nagoya-u.ac.jp/MHS/mhs2016-Top.html",

}

Phiri, CC, Ju, Z, Kubota, N & Liu, H 2017, Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware. in 2016 International Symposium on Micro-NanoMechatronics and Human Science (MHS). Institute of Electrical and Electronics Engineers Inc., 27th International Symposium on Micro-NanoMechatronics and Human Science, Nagoya, Japan, 28/11/16. https://doi.org/10.1109/MHS.2016.7824246

TY - GEN

T1 - Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware

AU - Phiri, Charles C.

AU - Ju, Zhaojie

AU - Kubota, Naoyuki

AU - Liu, Honghai

PY - 2017/1/19

Y1 - 2017/1/19

N2 - In this paper we continue with previous work by the authors implementing context-aware middleware to accelerate robot learning from demonstration, LfD. Specifically, we apply Fuzzy Q-Learning, FQL, reinforcement learning strategy to enhance the learning experience of the robot. Typically, fuzzy techniques allow the robot to make decisions without the need for an exhaustive map of the world. FQL, approximates the observable configuration space allowing the robot to overcome the high dimension challenge of feature decomposition and navigation in a stochastic environment.

AB - In this paper we continue with previous work by the authors implementing context-aware middleware to accelerate robot learning from demonstration, LfD. Specifically, we apply Fuzzy Q-Learning, FQL, reinforcement learning strategy to enhance the learning experience of the robot. Typically, fuzzy techniques allow the robot to make decisions without the need for an exhaustive map of the world. FQL, approximates the observable configuration space allowing the robot to overcome the high dimension challenge of feature decomposition and navigation in a stochastic environment.

U2 - 10.1109/MHS.2016.7824246

DO - 10.1109/MHS.2016.7824246

M3 - Conference contribution

SN - 978-1-5090-2786-6

BT - 2016 International Symposium on Micro-NanoMechatronics and Human Science (MHS)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th International Symposium on Micro-NanoMechatronics and Human Science

Y2 - 28 November 2016 through 30 November 2016

ER -

Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware

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