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2017 | OriginalPaper | Chapter

Self-improving Robot Action Management System with Probabilistic Graphical Model Based on Task Related Memories

Authors : Yuki Furuta, Yuto Inagaki, Kei Okada, Masayuki Inaba

Published in: Intelligent Autonomous Systems 14

Publisher: Springer International Publishing

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Abstract

Robots on home environment have to deal with their environment that changes every time they perform tasks. In order to reduce improving descriptions for task and action planning manually, we propose a new framework to use probabilistic graphical model, which enables robot agent know which action in the task affects the result of the task the most, and by improving parameters of the action robot can maintain high success rate of tasks. Our framework let robot infer failure action of tasks using data from early task performance which are automatically recorded and retrieved with high-level data retrieval query interface. We evaluated our approach using mobile manipulation robot PR2 on daily assistive environment and task.

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previous chapter Mechanism Allowing a Mobile Robot to Apply a Large Force to the Environment
next chapter View-Based Teaching/Playback with Photoelasticity for Force-Control Tasks
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Metadata
Title
Self-improving Robot Action Management System with Probabilistic Graphical Model Based on Task Related Memories
Authors
Yuki Furuta
Yuto Inagaki
Kei Okada
Masayuki Inaba
Copyright Year
2017
Book
Intelligent Autonomous Systems 14

Print ISBN: 978-3-319-48035-0

Electronic ISBN: 978-3-319-48036-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-48036-7_59

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