A Review on Robot Manipulation Methods in Human-Robot Interactions

TL;DR

This review paper discusses recent advances in autonomous robot manipulation methods within human-robot interaction, emphasizing deep reinforcement learning and imitation learning for complex, adaptive, and stable robot control in uncertain environments.

Contribution

It provides a comprehensive comparison of reinforcement learning and imitation learning algorithms, highlighting their roles in advancing autonomous robot manipulation.

Findings

Deep Reinforcement Learning enables complex task execution in unpredictable environments.

Imitation Learning simplifies robot training by removing reward function design.

The paper compares key algorithms and features of both learning approaches.

Abstract

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.