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We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
“The Columbia grasp database”
C. Goldfeder, M. Ciocarlie, Hao Dang and P.. Allen · 2009
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“Data-driven grasping with partial sensor data”
Corey Goldfeder et al · 2009
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“Efficient grasping from rgbd images: Learning using a new rectangle representation”
Yun Jiang, Stephen Moseson and Ashutosh Saxena · 2011
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“6DOF Grasp Planning by Optimizing a Deep Learning Scoring Function”
Yilun Zhou and Kris Hauser · 2011
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“Physics-based grasp planning through clutter”
Mehmet. Dogar, Kaijen Hsiao, Matei Ciocarlie and Siddhartha. Srinivasa · 2012
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“Unified particle physics for real-time applications”
Miles Macklin, Matthias M“”uller, Nuttapong Chentanez and Tae-Yong Kim · 2014
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“Leveraging Big Data for Grasp Planning”
D. Kappler, B. Bohg and S. Schaal · 2015
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“Deep learning for detecting robotic grasps”
Ian Lenz, Honglak Lee and Ashutosh Saxena · 2015
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“Real-time grasp detection using convolutional neural networks”
Joseph Redmon and Anelia Angelova · 2015
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“Semantically-enriched 3d models for common-sense knowledge”
Manolis Savva, Angel Chang and Pat Hanrahan · 2015
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“Recent data sets on object manipulation: A survey”
Yongqiang Huang, Matteo Bianchi, Minas Liarokapis and Yu Sun · 2016
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Sergey Levine, Peter Pastor, Alex Krizhevsky and Deirdre Quillen · 2016
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“Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours”
Lerrel Pinto and Abhinav Gupta · 2016
Earlier work this paper cites.
“Robot grasp detection using multimodal deep convolutional neural networks”
Zhichao Wang, Zhiqi Li, Bin Wang and Hong Liu · 2016
Cited alongside, same era.
“RGB-D object recognition and grasp detection using hierarchical cascaded forests”
Umar Asif, Mohammed Bennamoun and Ferdous Sohel · 2017
Cited alongside, same era.
“A hybrid deep architecture for robotic grasp detection”
Di Guo et al · 2017
Cited alongside, same era.
“Robotic grasp detection using deep convolutional neural networks”
Sulabh Kumra and Christopher Kanan · 2017
Cited alongside, same era.
“Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics”
Jeffrey Mahler et al · 2017
Cited alongside, same era.
“Grasp pose detection in point clouds”
Andreas ten Pas, Marcus Gualtieri, Kate Saenko and Robert Platt · 2017
“Robust watertight manifold surface generation method for shapenet models”
Jingwei Huang, Hao Su and Leonidas Guibas · 2018
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“Domain randomization and generative models for robotic grasping”
Josh Tobin et al · 2018
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“Roi-based robotic grasp detection in object overlapping scenes using convolutional neural network”
Hanbo Zhang, Xuguang Lan, Xinwen Zhou and Nanning Zheng · 2018
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“Fully convolutional grasp detection network with oriented anchor box”
Xinwen Zhou et al · 2018
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“Seeing What a GAN Cannot Generate”
David Bau et al · 2019
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Cited alongside, same era.
“An integrated simulator and dataset that combines grasping and vision for deep learning”
Matthew Veres, Medhat Moussa and Graham Taylor · 2017
Cited alongside, same era.
“Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations”
Xinchen Yan et al · 2017
Cited alongside, same era.
“GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices.”
Umar Asif, Jianbin Tang and Stefan Harrer · 2018
Cited alongside, same era.
“Real-world multiobject, multigrasp detection”
Fu-Jen Chu, Ruinian Xu and Patricio Vela · 2018
Cited alongside, same era.
“Jacquard: A large scale dataset for robotic grasp detection”
Amaury Depierre, Emmanuel Dellandr“’ea and Liming Chen · 2018
Cited alongside, same era.
“Panda”, 2018
Franka Emika · 2018
Cited alongside, same era.
“Reach: Reducing false negatives in robot grasp planning with a robust efficient area contact hypothesis model”
Michael Danielczuk et al · 2019
Later among the works it cites.
“A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-Based Grasp Data Set”
Clemens Eppner, Arsalan Mousavian and Dieter Fox · 2019
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“Diverse Image Synthesis from Semantic Layouts via Conditional IMLE”
K. Li, T. Zhang and J. Malik · 2019
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“6-DOF GraspNet: Variational Grasp Generation for Object Manipulation”
A. Mousavian, C. Eppner and D. Fox · 2019
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“Photoshape: photorealistic materials for large-scale shape collections”
Keunhong Park, Konstantinos Rematas, Ali Farhadi and Steven Seitz · 2019
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“GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping”
Hao-Shu Fang, Chenxi Wang, Minghao Gou and Cewu Lu · 2020
Closest in time.
“EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation”
D. Morrison, P. Corke and J. Leitner · 2020
Closest in time.
“6-DOF Grasping for Target-driven Object Manipulation in Clutter”
Adithya Murali et al · 2020
Closest in time.