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 the 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 the 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 article 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, this article discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of this article is to provide a comprehensive guide to the growing field of IL in robotics and AI.

中文翻译：

---

模仿学习调查：算法、最新进展和挑战


近年来，机器人和人工智能（AI）系统的发展令人瞩目。随着这些系统的不断发展，它们被用于日益复杂和非结构化的环境中，例如自动驾驶、空中机器人和自然语言处理。因此，像强化学习 (RL) 中那样手动编程它们的行为或通过奖励函数定义它们的行为变得极其困难。这是因为此类环境需要高度的灵活性和适应性，因此很难指定一组可以考虑所有可能情况的最佳规则或奖励信号。在这样的环境中，通过模仿来学习专家的行为通常更有吸引力。这就是模仿学习 (IL) 发挥作用的地方 - 通过模仿专家的行为（通过演示提供）来学习所需行为的过程。本文旨在介绍 IL 并概述其基本假设和方法。它还详细描述了该领域的最新进展和新兴研究领域。此外，本文还讨论了研究人员如何解决与 IL 相关的常见挑战，并为未来的研究提供了潜在的方向。总的来说，本文的目标是为机器人和人工智能领域不断发展的 IL 领域提供全面的指南。