KFU. Card publication. Transfer of learned exploration strategies of a mobile robot from a simulated to real environments

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

TRANSFER OF LEARNED EXPLORATION STRATEGIES OF A MOBILE ROBOT FROM A SIMULATED TO REAL ENVIRONMENTS

Form of presentation

Articles in international journals and collections

Year of publication

2019

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Authors, employees of KFU

Magid Evgeniy Arkadevich, author

Sagitov Artur Gazizovich, author

Authors, postgraduates KFU

Sagitov Artur Gazizovich, postgraduate kfu

Bibliographic description in the original language

Sagitov Artur, Takano Tetsuto, Muto Shohei, Magid Evgeni. Transfer of learned exploration strategies of a mobile robot from a simulated to real environments//ICAROB 2019: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS. - 2019. - Vol., Is.. - P.120-123.

Annotation

Reinforcement learning based approaches show promises in various robotic applications, but a significant amount of time and resources are required for a robot to learn optimal behavior. Using virtual environments, we could significantly speed up and improve performance of a target task. We implemented a reinforcement learning based exploration algorithm for a mobile robot, training in Gazebo environment and transferring learned strategy to a real robot. We show that it is convenient and appropriate to use simulation to train strategies for mobile robots.

Keywords

navigation; algorithm; mobile robots; reinforcement learning; exploration

The name of the journal

ICAROB 2019: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=196702&p_lang=2

Resource files

File name	Size (MB)	Format
F_2019_Transfer_of_learned_exploration_strategies_of_a_mobile_robot_from_a_simulated_to_real_environments..pdf	0,33	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Magid Evgeniy Arkadevich	ru_RU
dc.contributor.author	Sagitov Artur Gazizovich	ru_RU
dc.contributor.author	Sagitov Artur Gazizovich	ru_RU
dc.contributor.author	Sagitov Artur Gazizovich	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Sagitov Artur, Takano Tetsuto, Muto Shohei, Magid Evgeni. Transfer of learned exploration strategies of a mobile robot from a simulated to real environments//ICAROB 2019: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS. - 2019. - Vol., Is.. - P.120-123.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=196702&p_lang=2	ru_RU
dc.description.abstract	ICAROB 2019: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS	ru_RU
dc.description.abstract	Reinforcement learning based approaches show promises in various robotic applications, but a significant amount of time and resources are required for a robot to learn optimal behavior. Using virtual environments, we could significantly speed up and improve performance of a target task. We implemented a reinforcement learning based exploration algorithm for a mobile robot, training in Gazebo environment and transferring learned strategy to a real robot. We show that it is convenient and appropriate to use simulation to train strategies for mobile robots.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Transfer of learned exploration strategies of a mobile robot from a simulated to real environments	ru_RU
dc.type	Articles in international journals and collections	ru_RU