AUTHOR=Xie Zhen , Liang Xinquan , Roberto Canale 

TITLE=Learning-based robotic grasping: A review

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2023.1038658

DOI=10.3389/frobt.2023.1038658

ISSN=2296-9144

ABSTRACT=As the personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as Logistics, Fast-Moving Consumer Goods (FMCG), Food Delivery, etc., these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are therefore not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate High Mix and Low Volume (HMLV) manufacturing scenarios. In this paper, we review the recent development of Learning-based Robotic Grasping techniques from a corpus of over 120 papers. Besides addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled Grasping, which hinder the robotization in the aforementioned industries.     
Besides 3D object segmentation benchmarks and learning-based grasping benchmarks, we have also done a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes, and shapes and even can handle fragile objects. In general, robotic grasping can achieve better flexibility and adaptability, when equipped with learning algorithms.