A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges

by   Maryam Zare, et al.

In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through reward functions (as done in reinforcement learning (RL)) has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations. This paper aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, the paper discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.


page 1

page 2

page 6

page 8

page 11


An Algorithmic Perspective on Imitation Learning

As robots and other intelligent agents move from simple environments and...

Imitation Learning: Progress, Taxonomies and Opportunities

Imitation learning aims to extract knowledge from human experts' demonst...

Third-Person Imitation Learning

Reinforcement learning (RL) makes it possible to train agents capable of...

A Concise Introduction to Reinforcement Learning in Robotics

One of the biggest hurdles robotics faces is the facet of sophisticated ...

Goal-conditioned Imitation Learning

Designing rewards for Reinforcement Learning (RL) is challenging because...

Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks

A longstanding goal of artificial intelligence is to create artificial a...

The Virtuous Machine - Old Ethics for New Technology?

Modern AI and robotic systems are characterized by a high and ever-incre...

Please sign up or login with your details

Forgot password? Click here to reset