GRASP
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Grasping, in robotics and AI, refers to the process by which a robotic system physically acquires and secures an object using an end-effector such as a gripper or multi-fingered hand. It encompasses the full pipeline from perception—detecting object geometry, pose, and material properties via cameras, depth sensors, or tactile feedback—to planning stable contact configurations and executing the physical grip. Grasping approaches range from classical analytical methods, which use force-closure and contact mechanics to compute optimal finger placements, to modern data-driven techniques that train deep neural networks on large synthetic or real-world datasets to predict grasp candidates directly from images or point clouds. The field spans hardware design, including soft and underactuated grippers, as well as brain-machine interfaces for prosthetic control. Grasping is foundational to robotics because it gates nearly every manipulation task—assembly, surgery, logistics, and assistive care—making robust, generalizable grasp synthesis a central benchmark for evaluating a robot's ability to interact meaningfully with the physical world.
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Top Cited Papers
Soft Robotic Grippers
Jun Shintake, Vito Cacucciolo, Dario Floreano, Herbert Shea
Citations: 1850 • 2018
Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates
Jose M. Carmena, Mikhail Lebedev, Roy E. Crist, Joseph E. O’Doherty, David M. Santucci, Dragan F. Dimitrov, Parag G. Patil, Craig S. Henriquez, Miguel A. L. Nicolelis
Citations: 1781 • 2003
On grasp choice, grasp models, and the design of hands for manufacturing tasks
Mark R. Cutkosky
Citations: 1494 • 1989
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Pablo Aparicio, Ken Goldberg
Citations: 1162 • 2017
Learning the signatures of the human grasp using a scalable tactile glove
Subramanian Sundaram, Petr Kellnhofer, Yunzhu Li, Jun-Yan Zhu, Antonio Torralba, Wojciech Matusik
Citations: 1118 • 2019
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
Lerrel Pinto, Abhinav Gupta
Citations: 1099 • 2016
A novel type of compliant and underactuated robotic hand for dexterous grasping
Raphael Deimel, Oliver Brock
Citations: 1055 • 2015
GraspIt!
Andrew Miller, Peter K. Allen
Citations: 1028 • 2004
Robotic Grasping of Novel Objects using Vision
Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng
Citations: 948 • 2008
Real-time grasp detection using convolutional neural networks
Joseph Redmon, Anelia Angelova
Citations: 912 • 2015
Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity
Antonio Bicchi
Citations: 889 • 2000
Data-Driven Grasp Synthesis—A Survey
Citations: 848 • 2014
Tactile sensing in dexterous robot hands — Review
Zhanat Kappassov, Juan Antonio Corrales Ramón, Véronique Perdereau
Citations: 748 • 2015
Automatic grasp planning using shape primitives
Andrew Miller, Steffen Knoop, Henrik I. Christensen, Peter K. Allen
Citations: 715 • 2004
Robot Grasp Synthesis Algorithms: A Survey
K.B. Shimoga
Citations: 666 • 1996
Efficient grasping from RGBD images: Learning using a new rectangle representation
Jiang Yun, Stephen Moseson, Ashutosh Saxena
Citations: 662 • 2011
Robot-based hand motor therapy after stroke
C. D. Takahashi, Lucy Der-Yeghiaian, Vu Le, Rehan R. Motiwala, Steven C. Cramer
Citations: 629 • 2007
A brain-computer interface that evokes tactile sensations improves robotic arm control
Sharlene N. Flesher, John E. Downey, Jeffrey M. Weiss, Christopher Hughes, Angelica J. Herrera, Elizabeth C. Tyler‐Kabara, Michael L. Boninger, Jennifer L. Collinger, Robert A. Gaunt
Citations: 579 • 2021
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pástor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine
Citations: 575 • 2018
Time-Contrastive Networks: Self-Supervised Learning from Video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, Google Brain
Citations: 555 • 2018