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Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object.
C. Borst, M. Fischer, and G. Hirzinger, “Grasp planning: How to choose a suitable task wrench space,” in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004 , vol. 1, Apr. 2004, pp. 319–325 Vol.1
2004
Earlier work this paper cites.
R. Haschke, J. Steil, I. Steuwer, and H. Ritter, “Task-oriented quality measures for dextrous grasping,” in 2005 International Symposium on Computational Intelligence in Robotics and Automation , Jun. 2005, pp. 689–694
2005
Earlier work this paper cites.
D. Song, K. Huebner, V. Kyrki, and D. Kragic, “Learning task constraints for robot grasping using graphical models,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems , Oct. 2010, pp. 1579–1585
2010
Earlier work this paper cites.
S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition , vol. 47, no. 6, pp. 2280–2292, Jun. 2014
2014
Earlier work this paper cites.
D. Song, C. H. Ek, K. Huebner, and D. Kragic, “Task-Based Robot Grasp Planning Using Probabilistic Inference,” IEEE Transactions on Robotics , vol. 31, no. 3, pp. 546–561, Jun. 2015
2015
Earlier work this paper cites.
I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, Apr. 2015
2015
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning Structured Output Representation using Deep Conditional Generative Models,” in Advances in Neural Information Processing Systems , vol. 28. Curran Associates, Inc., 2015
2015
Earlier work this paper cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The YCB object and Model set: Towards common benchmarks for manipulation research,” in 2015 International Conference on Advanced Robotics (ICAR) , Jul. 2015, pp. 510–517
2015
Earlier work this paper cites.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. Aparicio, and K. Goldberg, “Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics,” in Robotics: Science and Systems XIII . Robotics: Science and Systems Foundation, Jul. 2017
2017
Earlier work this paper cites.
R. Detry, J. Papon, and L. Matthies, “Task-oriented grasping with semantic and geometric scene understanding,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sep. 2017, pp. 3266–3273
2017
Earlier work this paper cites.
M. Kokic, J. A. Stork, J. A. Haustein, and D. Kragic, “Affordance detection for task-specific grasping using deep learning,” in 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) , Nov. 2017, pp. 91–98
2017
Cited alongside, same era.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,” in Advances in Neural Information Processing Systems , vol. 30. Curran Associates, Inc., 2017
2017
Cited alongside, same era.
D. Morrison, J. Leitner, and P. Corke, “Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach,” in Robotics: Science and Systems XIV . Robotics: Science and Systems Foundation, Jun. 2018
2018
Cited alongside, same era.
R. Antonova, M. Kokic, J. A. Stork, and D. Kragic, “Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation,” in Proceedings of The 2nd Conference on Robot Learning . PMLR, Oct. 2018, pp. 641–650
2018
K. Fang, Y. Zhu, A. Garg, A. Kurenkov, V. Mehta, L. Fei-Fei, and S. Savarese, “Learning task-oriented grasping for tool manipulation from simulated self-supervision,” The International Journal of Robotics Research , vol. 39, no. 2-3, pp. 202–216, Mar. 2020
2020
Later among the works it cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Seattle, WA, USA: IEEE, Jun. 2020, pp. 11 441–11 450
2020
Later among the works it cites.
D. Morrison, P. Corke, and J. Leitner, “EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4368–4375, Jul. 2020
2020
Later among the works it cites.
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , May 2021, pp. 13 438–13 444
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Cited alongside, same era.
A. Depierre, E. Dellandréa, and L. Chen, “Jacquard: A Large Scale Dataset for Robotic Grasp Detection,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2018, pp. 3511–3516
2018
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-DOF GraspNet: Variational Grasp Generation for Object Manipulation,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Seoul, Korea (South): IEEE, Oct. 2019, pp. 2901–2910
2019
Cited alongside, same era.
S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8709–8719
2019
Cited alongside, same era.
C. Eppner, A. Mousavian, and D. Fox, “A billion ways to grasps - an evaluation of grasp sampling schemes on a dense, physics-based grasp data set,” in Proceedings of the International Symposium on Robotics Research (ISRR) , Hanoi, Vietnam, 2019
2019
Cited alongside, same era.
M. Kokic, D. Kragic, and J. Bohg, “Learning Task-Oriented Grasping From Human Activity Datasets,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3352–3359, Apr. 2020
2020
Cited alongside, same era.
W. Liu, A. Daruna, and S. Chernova, “Cage: Context-aware grasping engine,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2550–2556
2020
Cited alongside, same era.
2021
Later among the works it cites.
A. Murali, W. Liu, K. Marino, S. Chernova, and A. Gupta, “Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping,” in Proceedings of the 2020 Conference on Robot Learning . PMLR, Oct. 2021, pp. 1540–1557
2021
Later among the works it cites.
C. Eppner, A. Mousavian, and D. Fox, “ACRONYM: A Large-Scale Grasp Dataset Based on Simulation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , May 2021, pp. 6222–6227
2021
Later among the works it cites.
J. Lundell, E. Corona, T. Nguyen Le, F. Verdoja, P. Weinzaepfel, G. Rogez, F. Moreno-Noguer, and V. Kyrki, “Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , May 2021, pp. 4495–4501
2021
Later among the works it cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning,” Aug. 2021
2021
Later among the works it cites.
T. N. Le, J. Lundell, F. J. Abu-Dakka, and V. Kyrki, “Deformation-Aware Data-Driven Grasp Synthesis,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3038–3045, Apr. 2022
2022
Later among the works it cites.