抓住
人工智能
计算机科学
功能可见性
过程(计算)
对象(语法)
强化学习
机器人学
机器人末端执行器
深度学习
集合(抽象数据类型)
夹持器
机器学习
人机交互
机器人
工程类
操作系统
机械工程
程序设计语言
作者
R. Newbury,Morris Gu,Lachlan Chumbley,Arsalan Mousavian,Clemens Eppner,Jürgen Leitner,Jeannette Bohg,Antonio Morales,Tamim Asfour,Danica Kragić,Dieter Fox,Akansel Cosgun
出处
期刊:IEEE Transactions on Robotics
[Institute of Electrical and Electronics Engineers]
日期:2023-06-13
卷期号:39 (5): 3994-4015
被引量:71
标识
DOI:10.1109/tro.2023.3280597
摘要
Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two “supporting methods” around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.
科研通智能强力驱动
Strongly Powered by AbleSci AI