КФУ. Карточка публикации. Transfer of learned exploration strategies of a mobile robot from a simulated to real environments

КАЗАНСКИЙ
ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ

RUS

- EN
- 中文
- ES

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

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2019

Язык

английский

Авторы, сотрудники КФУ

Магид Евгений Аркадьевич, автор

Сагитов Артур Газизович, автор

Авторы, аспиранты КФУ

Сагитов Артур Газизович, автор

Библиографическое описание на языке оригинала

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.

Аннотация

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.

Ключевые слова

navigation; algorithm; mobile robots; reinforcement learning; exploration

Название журнала

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

Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку

https://repository.kpfu.ru/?p_id=196702

Файлы ресурса

Название файла	Размер (Мб)	Формат
F_2019_Transfer_of_learned_exploration_strategies_of_a_mobile_robot_from_a_simulated_to_real_environments..pdf	0,33	pdf	посмотреть / скачать

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Магид Евгений Аркадьевич	ru_RU
dc.contributor.author	Сагитов Артур Газизович	ru_RU
dc.contributor.author	Сагитов Артур Газизович	ru_RU
dc.contributor.author	Сагитов Артур Газизович	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/?p_id=196702	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	Статьи в зарубежных журналах и сборниках	ru_RU