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General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.
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J. J. Kuffner · 2004
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The kit object models database: An object model database for object recognition, localization and manipulation in service robotics
A. Kasper, Z. Xue, and R. Dillmann · 2012
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2013
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Data-driven grasp synthesis-a survey
J. Bohg, A. Morales, T. Asfour, and D. Kragic · 2014
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Bigbird: A large-scale 3d database of object instances
A. Singh, J. Sha, K. S. Narayan, T. Achim, and P. Abbeel · 2014
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I. Lenz, H. Lee, and A. Saxena · 2015
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The ycb object and model set: Towards common benchmarks for manipulation research
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
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Leveraging big data for grasp planning
D. Kappler, J. Bohg, and S. Schaal · 2015
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High precision grasp pose detection in dense clutter
M. Gualtieri, A. ten Pas, K. Saenko, and R. Platt · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Deep learning a grasp function for grasping under gripper pose uncertainty
E. Johns, S. Leutenegger, and A. J. Davison · 2016
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Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. Aparicio, and K. Goldberg · 2017
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Robotic grasp detection using deep convolutional neural networks
S. Kumra and C. Kanan · 2017
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Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning
H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto · 2017
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Supervision via competition: Robot adversaries for learning tasks
L. Pinto, J. Davidson, and A. Gupta · 2017
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Learning object grasping for soft robot hands
Open3d: A modern library for 3d data processing
Q.-Y. Zhou, J. Park, and V. Koltun · 2018
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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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PointNetGPD: Detecting grasp configurations from point sets
H. Liang, X. Ma, S. Li, M. Görner, S. Tang, B. Fang, F. Sun, and J. Zhang · 2019
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Modeling grasp type improves learning-based grasp planning
Q. Lu and T. Hermans · 2019
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On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks
V. Satish, J. Mahler, and K. Goldberg · 2019
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C. Choi, W. Schwarting, J. DelPreto, and D. Rus · 2018
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Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
D. Morrison, J. Leitner, and P. Corke · 2018
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Learning 6-dof grasping interaction via deep geometry-aware 3d representations
X. Yan, J. Hsu, M. Khansari, Y. Bai, A. Pathak, A. Gupta, J. Davidson, and H. Lee · 2018
Cited alongside, same era.
Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser · 2018
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Evaluation of combined time-offset estimation and hand-eye calibration on robotic datasets
F. Furrer, M. Fehr, T. Novkovic, H. Sommer, I. Gilitschenski, and R. Siegwart · 2018
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R. M. Grassmann and J. Burgner-Kahrs · 2019
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Beyond top-grasps through scene completion
J. Lundell, F. Verdoja, and V. Kyrki · 2020
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6-DOF grasping for target-driven object manipulation in clutter
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox · 2020
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Learning continuous 3d reconstructions for geometrically aware grasping
M. Van der Merwe, Q. Lu, B. Sundaralingam, M. Matak, and T. Hermans · 2020
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Pybullet, a python module for physics simulation for games, robotics and machine learning, 2020
E. Coumans and Y. Bai · 2020
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