Robot manipulation is an important part of human-robot interaction technology. However, traditional pre-programmed methods can only accomplish simple and repetitive tasks. To enable effective communication between robots and humans, and to predict and adapt to uncertain environments, this paper reviews recent autonomous and adaptive learning in robotic manipulation algorithms. It includes typical applications and challenges of human-robot interaction, fundamental tasks of robot manipulation and one of the most widely used formulations of robot manipulation, Markov Decision Process. Recent research focusing on robot manipulation is mainly based on Reinforcement Learning and Imitation Learning. This review paper shows the importance of Deep Reinforcement Learning, which plays an important role in manipulating robots to complete complex tasks in disturbed and unfamiliar environments. With the introduction of Imitation Learning, it is possible for robot manipulation to get rid of reward function design and achieve a simple, stable and supervised learning process. This paper reviews and compares the main features and popular algorithms for both Reinforcement Learning and Imitation Learning.

翻译：机器人操控是人机交互技术的重要组成部分。然而，传统的预编程方法仅能完成简单重复的任务。为实现机器人与人类的有效沟通，并预测及适应不确定环境，本文综述了近期机器人操控算法中的自主与自适应学习方法。内容涵盖人机交互的典型应用及挑战、机器人操控的基本任务，以及最广泛使用的机器人操控形式化模型——马尔可夫决策过程。近期聚焦机器人操控的研究主要基于强化学习与模仿学习。本综述展示了深度强化学习的重要性，它在受干扰和陌生环境中协助机器人完成复杂任务方面发挥着关键作用。随着模仿学习的引入，机器人操控得以摆脱奖励函数设计，实现简单、稳定且有监督的学习过程。本文对强化学习与模仿学习的主要特征及主流算法进行了综述与比较。