  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.
