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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Bibliographic Details
Published in:IEEE robotics and automation letters 2020-04, Vol.5 (2), p.3508-3515
Main Authors: Liu, Boyi, Wang, Lujia, Liu, Ming, Xu, Cheng-Zhong
Format: Article
Language:English
Subjects:
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
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Summary: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, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2976321