Research Area:  Machine Learning

Abstract:

Imitation learning (IL) aims to extract knowledge from human experts’ demonstrations or artificially created agents to replicate their behaviors. It promotes interdisciplinary communication and real-world automation applications. However, the process of replicating behaviors still exhibits various problems, such as the performance is highly dependent on the demonstration quality, and most trained agents are limited to perform well in task-specific environments. In this survey, we provide an insightful review on IL. We first introduce the background knowledge from development history and preliminaries, followed by presenting different taxonomies within IL and key milestones of the field. We then detail challenges in learning strategies and present research opportunities with learning policy from suboptimal demonstration, voice instructions, and other associated optimization schemes.

Keywords:  
imitation learning
interdisciplinary communication
learning policy
suboptimal demonstration
voice instructions
optimization schemes

Author(s) Name:  Boyuan Zheng, Sunny Verma, Jianlong Zhou, Ivor W. Tsang, Fang Chen

Publisher name:  IEEE

DOI:  https://doi.org/10.1109/TNNLS.2022.3213246

Volume Information:  -

Paper Link: https://ieeexplore.ieee.org/document/9927439