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Humans are capable of learning a new behavior by observing others perform theskill. Robots can also implement this by imitation learning. Furthermore, ifwith external guidance, humans will master the new behavior more efficiently.So how can robots implement this? To address the issue, we present FederatedImitation Learning (FIL) in the paper. Firstly, a knowledge fusion algorithmdeployed on the cloud for fusing knowledge from local robots is presented.Then, effective transfer learning methods in FIL are introduced. With FIL, arobot is capable of utilizing knowledge from other robots to increase itsimitation learning. FIL considers information privacy and data heterogeneitywhen robots share knowledge. It is suitable to be deployed in cloud roboticsystems. Finally, we conduct experiments of a simplified self-driving task forrobots (cars). The experimental results demonstrate that FIL is capable ofincreasing imitation learning of local robots in cloud robotic systems.

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data