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A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task.
Strategies for human-in-the-loop robotic grasping
Leeper, A. E.; Hsiao, K.; Ciocarlie, M.; Takayama, L.; and Gossow, D. 2012 · 2012
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Reducing the barrier to entry of complex robotic software: a moveit! case study
Coleman, D.; Sucan, I.; Chitta, S.; and Correll, N. 2014 · 2014
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
Earlier work this paper cites.
One-shot learning and generation of dexterous grasps for novel objects
Kopicki, M.; Detry, R.; Adjigble, M.; Stolkin, R.; Leonardis, A.; and Wyatt, J. L. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Interactive scene segmentation for efficient human-in-the-loop robot manipulation
Butler, D. J.; Elliot, S.; and Cakmak, M. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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Affordancenet: An end-to-end deep learning approach for object affordance detection
Do, T.-T.; Nguyen, A.; and Reid, I. 2018 · 2018
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Reptile: a scalable metalearning algorithm
Nichol, A.; and Schulman, J. 2018 · 2018
Cited alongside, same era.
Learning to grasp from a single demonstration
Van Molle, P.; Verbelen, T.; De Coninck, E.; De Boom, C.; Simoens, P.; and Dhoedt, B. 2018 · 2018
Cited alongside, same era.
Pointnetgpd: Detecting grasp configurations from point sets
Liang, H.; Ma, X.; Li, S.; Görner, M.; Tang, S.; Fang, B.; Sun, F.; and Zhang, J. 2019 · 2019
Cited alongside, same era.
6-dof graspnet: Variational grasp generation for object manipulation
Mousavian, A.; Eppner, C.; and Fox, D. 2019 · 2019
Cited alongside, same era.
Graspnet-1billion: A large-scale benchmark for general object grasping
Fang, H.-S.; Wang, C.; Gou, M.; and Lu, C. 2020 · 2020
Cited alongside, same era.
Cage: Context-aware grasping engine
Liu, W.; Daruna, A.; and Chernova, S. 2020 · 2020
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f-brs: Rethinking backpropagating refinement for interactive segmentation
Sofiiuk, K.; Petrov, I.; Barinova, O.; and Konushin, A. 2020 · 2020
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
Sundermeyer, M.; Mousavian, A.; Triebel, R.; and Fox, D. 2021 · 2021
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DemoGrasp: Few-shot learning for robotic grasping with human demonstration
Wang, P.; Manhardt, F.; Minciullo, L.; Garattoni, L.; Meier, S.; Navab, N.; and Busam, B. 2021 · 2021
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Attribute-based Robotic Grasping with One-grasp Adaptation
Yang, Y.; Liu, Y.; Liang, H.; Lou, X.; and Choi, C. 2021 · 2021
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Learning prohibited and authorised grasping locations from a few demonstrations
Hélénon, F.; Bimont, L.; Nyiri, E.; Thiery, S.; and Gibaru, O. 2020 · 2020
Cited alongside, same era.
Leveraging depth data in remote robot teleoperation interfaces for general object manipulation
Kent, D.; Saldanha, C.; and Chernova, S. 2020 · 2020
Cited alongside, same era.
Guo, W.; Li, W.; Hu, Z.; and Gan, Z. 2022 · 2022
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Task-grasping from human demonstration
Saito, D.; Sasabuchi, K.; Wake, N.; Takamatsu, J.; Koike, H.; and Ikeuchi, K. 2022 · 2022
Later among the works it cites.