  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.
