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Recently, end-to-end learning frameworks are gaining prevalence in the field of robot control.
Planning collision-free motions for pick-and-place operations
Rodney A Brooks · 1983
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Constructing force-closure grasps
Van-Duc Nguyen · 1988
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Task-level planning of pick-and-place robot motions
Tomás Lozano-Pérez, Joseph L. Jones, Emmanuel Mazer, and Patrick A. O’Donnell · 1989
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Reducing uncertainty of objects by robot pushing
Zdravko Balorda · 1990
Earlier work this paper cites.
Automatic planning of robot pushing operations
Zdravko Balorda · 1993
Earlier work this paper cites.
Object handling using two arms without grasping
Xiaoping Yun · 1993
Earlier work this paper cites.
Robot grasp synthesis algorithms: A survey
Karun B Shimoga · 1996
Earlier work this paper cites.
Stable pushing: Mechanics, controllability, and planning
Kevin M Lynch and Matthew T Mason · 1996
Earlier work this paper cites.
Robotic grasping and contact: A review
Antonio Bicchi and Vijay Kumar · 2000
Earlier work this paper cites.
Automatic grasp planning using shape primitives
Andrew T Miller, Steffen Knoop, Henrik I Christensen, and Peter K Allen · 2003
Earlier work this paper cites.
Graspit! a versatile simulator for robotic grasping
Andrew T Miller and Peter K Allen · 2004
Cited alongside, same era.
Object identification with tactile sensors using bag-of-features
Alexander Schneider, Jürgen Sturm, Cyrill Stachniss, Marco Reisert, Hans Burkhardt, and Wolfram Burgard · 2009
Cited alongside, same era.
A framework for push-grasping in clutter
Mehmet Dogar and Siddhartha Srinivasa · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen · 2016
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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Ian Lenz, Honglak Lee, and Ashutosh Saxena · 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.
Perceiving, learning, and exploiting object affordances for autonomous pile manipulation
Dov Katz, Arun Venkatraman, Moslem Kazemi, J Andrew Bagnell, and Anthony Stentz · 2014
Cited alongside, same era.
R-cnns for pose estimation and action detection
Georgia Gkioxari, Bharath Hariharan, Ross Girshick, and Jitendra Malik · 2014
Cited alongside, same era.
Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2015
Cited alongside, same era.
Closest in time.
Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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A convex polynomial force-motion model for planar sliding: Identification and application
Jiaji Zhou, Robert Paolini, J Andrew Bagnell, and Matthew T Mason · 2016
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” what happens if…” learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Iasonas Kokkinos · 2016
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