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Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability.
Constructing force-closure grasps
Van-Duc Nguyen · 1988
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A mathematical introduction to robotic manipulation
Richard M Murray, Zexiang Li, S Shankar Sastry, and S Shankara Sastry · 1994
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Evolutionary robotics and the radical envelope-of-noise hypothesis
Nick Jakobi · 1997
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Robotic grasping and contact: A review
Antonio Bicchi and Vijay Kumar · 2000
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Automatic grasp planning using shape primitives
Andrew T Miller, Steffen Knoop, Henrik I Christensen, and Peter K Allen · 2003
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Graspit! a versatile simulator for robotic grasping
Andrew T Miller and Peter K Allen · 2004
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Using experience for assessing grasp reliability
Antonio Morales, Eris Chinellato, Andrew H Fagg, and Angel P Del Pobil · 2004
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An svm learning approach to robotic grasping
Raphael Pelossof, Andrew Miller, Peter Allen, and Tony Jebara · 2004
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Grasp planning via decomposition trees
Corey Goldfeder, Peter K Allen, Claire Lackner, and Raphael Pelossof · 2007
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Learning object affordances: from sensory–motor coordination to imitation
Luis Montesano, Manuel Lopes, Alexandre Bernardino, and José Santos-Victor · 2008
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Robotic grasping of novel objects using vision
Ashutosh Saxena, Justin Driemeyer, and Andrew Y Ng · 2008
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Learning grasp strategies with partial shape information
Ashutosh Saxena, Lawson LS Wong, and Andrew Y Ng · 2008
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Learning to grasp objects with multiple contact points
Quoc V Le, David Kamm, Arda F Kara, and Andrew Y Ng · 2010
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Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
Stefan Hinterstoisser, Stefan Holzer, Cedric Cagniart, Slobodan Ilic, Kurt Konolige, Nassir Navab, and Vincent Lepetit · 2011
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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A neural autoregressive topic model
Hugo Larochelle and Stanislas Lauly · 2012
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On the manipulability ellipsoids of underactuated robotic hands with compliance
Domenico Prattichizzo, Monica Malvezzi, Marco Gabiccini, and Antonio Bicchi · 2012
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From caging to grasping
Alberto Rodriguez, Matthew T Mason, and Steve Ferry · 2012
Cited alongside, same era.
On the synthesis of feasible and prehensile robotic grasps
Carlos Rosales, Raúl Suárez, Marco Gabiccini, and Antonio Bicchi · 2012
Cited alongside, same era.
An overview of 3d object grasp synthesis algorithms
Anis Sahbani, Sahar El-Khoury, and Philippe Bidaud · 2012
Cited alongside, same era.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Cited alongside, same era.
Pose error robust grasping from contact wrench space metrics
Jonathan Weisz and Peter K Allen · 2012
Cited alongside, same era.
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra · 2013
Deep learning a grasp function for grasping under gripper pose uncertainty
Edward Johns, Stefan Leutenegger, and Andrew J Davison · 2016
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Supervision via competition: Robot adversaries for learning tasks
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Cited alongside, same era.
Cloud-based robot grasping with the google object recognition engine
Ben Kehoe, Akihiro Matsukawa, Sal Candido, James Kuffner, and Ken Goldberg · 2013
Cited alongside, same era.
Multimodal blending for high-accuracy instance recognition
Ziang Xie, Arjun Singh, Justin Uang, Karthik S Narayan, and Pieter Abbeel · 2013
Cited alongside, same era.
Data-driven grasp synthesis—a survey
Jeannette Bohg, Antonio Morales, Tamim Asfour, and Danica Kragic · 2014
Cited alongside, same era.
Towards reliable grasping and manipulation in household environments
Matei Ciocarlie, Kaijen Hsiao, Edward Gil Jones, Sachin Chitta, Radu Bogdan Rusu, and Ioan A Şucan · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Lerrel Pinto, James Davidson, and Abhinav Gupta · 2016
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Grasping
Domenico Prattichizzo and Jeffrey C Trinkle · 2016
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(cad) 2 rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
Later among the works it cites.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2017
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Intel realsense stereoscopic depth cameras
Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, and Achintya Bhowmik · 2017
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Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
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Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, and Ken Goldberg · 2017
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Grasping and fixturing as submodular coverage problems
John D Schulman, Ken Goldberg, and Pieter Abbeel · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Learning a visuomotor controller for real world robotic grasping using easily simulated depth images
Ulrich Viereck, Andreas ten Pas, Kate Saenko, and Robert Platt · 2017
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Learning grasping interaction with geometry-aware 3d representations
Xinchen Yan, Mohi Khansari, Yunfei Bai, Jasmine Hsu, Arkanath Pathak, Arbhinav Gupta, James Davidson, and Honglak Lee · 2017
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Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge
Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker, Alberto Rodriguez, and Jianxiong Xiao · 2017
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Fangyi Zhang, Jürgen Leitner, Michael Milford, and Peter Corke · 2017
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