Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue,...

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Vydané v:	IEEE robotics and automation letters Ročník 5; číslo 2; s. 3508 - 3515
Hlavní autori:	Liu, Boyi, Wang, Lujia, Liu, Ming, Xu, Cheng-Zhong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Big data in robotics and automation Cloud computing Computational modeling deep learning in robotics and automation Fuses Knowledge Knowledge acquisition Knowledge management Learning Microstrip motion and path planning Robot sensing systems Robotics Robots Task analysis
ISSN:	2377-3766, 2377-3766
On-line prístup:	Získať plný text
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