Machine Learning and Soft Robotics

Abstract

In recent times, robotics and especially soft robotics has attracted a wide range of researchers and scientists. As oft robotics has an extensive number of advantages in the real environment, due to their less complex system and cost. Soft grippers are more adaptive as compared to the rigid robotic grippers. The grasping performance of soft robots can be improved without bringing major changes in the control inputs. Machine learning has played a vital role in improving the controls and increasing the number of applications of these kinds of robots in the real world. In this paper relevant research in modeling, design, intelligent control, sensing, and practical applications of soft robots has been discussed.