Fetching the paper…
Reading the bibliography…
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table.
Finding antipodal point grasps on irregularly shaped objects
I-Ming Chen and Joel W Burdick · 1993
Earlier work this paper cites.
On loss functions which minimize to conditional expected values and posterior probabilities
John W Miller, Rod Goodman, and Padhraic Smyth · 1993
Earlier work this paper cites.
Part pose statistics: Estimators and experiments
Ken Goldberg, Brian V Mirtich, Yan Zhuang, John Craig, Brian R Carlisle, and John Canny · 1999
Earlier work this paper cites.
The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
Earlier work this paper cites.
Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Grasping
Domenico Prattichizzo and Jeffrey C Trinkle · 2008
Earlier work this paper cites.
Robotic grasping of novel objects using vision
Ashutosh Saxena, Justin Driemeyer, and Andrew Y Ng · 2008
Earlier work this paper cites.
The columbia grasp database
Corey Goldfeder, Matei Ciocarlie, Hao Dang, and Peter K Allen · 2009
Earlier work this paper cites.
Learning grasping points with shape context
Jeannette Bohg and Danica Kragic · 2010
Earlier work this paper cites.
Collaborative grasp planning with multiple object representations
Peter Brook, Matei Ciocarlie, and Kaijen Hsiao · 2011
Earlier work this paper cites.
Data-driven grasping
Corey Goldfeder and Peter K Allen · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
Physical human interactive guidance: Identifying grasping principles from human-planned grasps
Ravi Balasubramanian, Ling Xu, Peter D Brook, Joshua R Smith, and Yoky Matsuoka · 2012
Earlier work this paper cites.
The kit object models database: An object model database for object recognition, localization and manipulation in service robotics
Alexander Kasper, Zhixing Xue, and Rüdiger Dillmann · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
Luis Montesano and Manuel Lopes · 2012
Earlier work this paper cites.
From caging to grasping
Alberto Rodriguez, Matthew T Mason, and Steve Ferry · 2012
Earlier work this paper cites.
Pose error robust grasping from contact wrench space metrics
Jonathan Weisz and Peter K Allen · 2012
Earlier work this paper cites.
3dnet: Large-scale object class recognition from cad models
Walter Wohlkinger, Aitor Aldoma, Radu B Rusu, and Markus Vincze · 2012
Earlier work this paper cites.
Learning a dictionary of prototypical grasp-predicting parts from grasping experience
Renaud Detry, Carl Henrik Ek, Marianna Madry, and Danica Kragic · 2013
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.
Classical grasp quality evaluation: New algorithms and theory
Florian T Pokorny and Danica Kragic · 2013
Cited alongside, same era.
Fast sequential Monte Carlo methods for counting and optimization
Reuven Y Rubinstein, Ad Ridder, and Radislav Vaisman · 2013
Cited alongside, same era.
Slam++: Simultaneous localisation and mapping at the level of objects
Renato F Salas-Moreno, Richard A Newcombe, Hauke Strasdat, Paul HJ Kelly, and Andrew J Davison · 2013
Cited alongside, same era.
Multimodal blending for high-accuracy instance recognition
Using geometry to detect grasp poses in 3d point clouds
Andreas ten Pas and Robert Platt · 2015
Later among the works it cites.
Generating multi-fingered robotic grasps via deep learning
Jacob Varley, Jonathan Weisz, Jared Weiss, and Peter Allen · 2015
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Later among the works it cites.
High precision grasp pose detection in dense clutter
Marcus Gualtieri, Andreas ten Pas, Kate Saenko, and Robert Platt · 2016
Later among the works it cites.
Team delft’s robot winner of the amazon picking challenge 2016
Carlos Hernandez, Mukunda Bharatheesha, Wilson Ko, Hans Gaiser, Jethro Tan, Kanter van Deurzen, Maarten de Vries, Bas Van Mil, Jeff van Egmond, Ruben Burger, et al · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Learning of grasp selection based on shape-templates
Alexander Herzog, Peter Pastor, Mrinal Kalakrishnan, Ludovic Righetti, Jeannette Bohg, Tamim Asfour, and Stefan Schaal · 2014
Cited alongside, same era.
Characterizations of noise in kinect depth images: A review
Tanwi Mallick, Partha Pratim Das, and Arun Kumar Majumdar · 2014
Cited alongside, same era.
Multimodal deep learning for robust rgb-d object recognition
Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin Riedmiller, and Wolfram Burgard · 2015
Cited alongside, same era.
Aligning 3d models to rgb-d images of cluttered scenes
Saurabh Gupta, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
Cited alongside, same era.
Later among the works it cites.
Deep learning a grasp function for grasping under gripper pose uncertainty
Edward Johns, Stefan Leutenegger, and Andrew J Davison · 2016
Later among the works it cites.
Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen · 2016
Later among the works it cites.
Detecting object affordances with convolutional neural networks
Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis · 2016
Later among the works it cites.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
Later among the works it cites.
Supervision via competition: Robot adversaries for learning tasks
Lerrel Pinto, James Davidson, and Abhinav Gupta · 2016
Later among the works it cites.
Learning depth-aware deep representations for robotic perception
Lorenzo Porzi, Samuel Rota Bulo, Adrian Penate-Sanchez, Elisa Ricci, and Francesc Moreno-Noguer · 2016
Later among the works it cites.
(cad)2 rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
Later among the works it cites.
Large-scale supervised learning of the grasp robustness of surface patch pairs
Daniel Seita, Florian T Pokorny, Jeffrey Mahler, Danica Kragic, Michael Franklin, John Canny, and Ken Goldberg · 2016
Later among the works it cites.
Adapting deep visuomotor representations with weak pairwise constraints
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, and Trevor Darrell · 2016
Later among the works it cites.
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 Jr, Alberto Rodriguez, and Jianxiong Xiao · 2016
Later among the works it cites.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2016
Later among the works it cites.
Design of parallel-jaw gripper tip surfaces for robust grasping
Menglong Guo, David V Gealy, Jacky Liang, Jeffrey Mahler, Aimee Goncalves, Stephen McKinley, and Ken Goldberg · 2017
Closest in time.
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, and Ken Goldberg · 2017
Closest in time.
Modeling grasp motor imagery through deep conditional generative models
Matthew Veres, Medhat Moussa, and Graham W Taylor · 2017
Closest in time.