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

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data Boyi Liu 1,4, Lujia Wang 1, Ming Liu 2 and Cheng-Zhong Xu 3 Abstract -- Humans are capable of learning a new behavior by observing others 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? T o address the issue, we present Federated Imitation Learning (FIL) in the paper . Firstly, a knowledge fusion algorithm is proposed for the cloud fusing knowledge from local robots. Then, a knowledge transfer scheme is presented 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 training efficiency. FIL considers information privacy and data heterogeneity when robots share knowledge. It is suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a simplified self-driving task for robots (cars). The experimental results demonstrate that FIL increases imitation learning efficiency and accuracy of local robots in cloud robotic systems. I. INTRODUCTION In tradition imitation learning scenarios, demonstrations provide a descriptive medium for specifying robotic tasks. Prior work has shown that robots can acquire a range of complex skills through demonstration, such as table tennis [1], drawer opening [2], and multistage manipulation tasks [3]. Nevertheless, there exists a number of problems in the application of imitation learning.