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Robotic grasping in clutters is a fundamental task in robotic manipulation.
Nguyen, V.D.: Constructing force-closure grasps. The International Journal of Robotics Research (1988)
1988
Earlier work this paper cites.
Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. The International Journal of Robotics Research (2008)
2008
Earlier work this paper cites.
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., et al.: Ros: an open-source robot operating system. In: ICRA workshop on open source software. vol. 3, p. 5. Kobe, Japan (2009)
2009
Earlier work this paper cites.
Le, Q.V., Kamm, D., Kara, A.F., Ng, A.Y.: Learning to grasp objects with multiple contact points. In: IEEE International Conference on Robotics and Automation (2010)
2010
Earlier work this paper cites.
Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from rgbd images: Learning using a new rectangle representation. In: IEEE International Conference on Robotics and Automation (2011)
2011
Earlier work this paper cites.
Fischinger, D., Vincze, M.: Empty the basket-a shape based learning approach for grasping piles of unknown objects. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2012)
2012
Earlier work this paper cites.
Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., Schaal, S.: Template-based learning of grasp selection. In: IEEE International Conference on Robotics and Automation (2012)
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
2015
Earlier work this paper cites.
Kappler, D., Bohg, J., Schaal, S.: Leveraging big data for grasp planning. In: IEEE International Conference on Robotics and Automation (2015)
2015
Earlier work this paper cites.
Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. The International Journal of Robotics Research (2015)
2015
Earlier work this paper cites.
Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In: IEEE International Conference on Robotics and Automation (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks (2015)
2015
Earlier work this paper cites.
Correll, N., Bekris, K.E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J.M., Wurman, P.R.: Analysis and observations from the first amazon picking challenge. IEEE Transactions on Automation Science and Engineering (2016)
2016
Earlier work this paper cites.
Gualtieri, M., Ten Pas, A., Saenko, K., Platt, R.: High precision grasp pose detection in dense clutter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2016)
2016
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space (2017)
2017
Earlier work this paper cites.
Ten Pas, A., Gualtieri, M., Saenko, K., Platt, R.: Grasp pose detection in point clouds. The International Journal of Robotics Research (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need (2017)
2017
Earlier work this paper cites.
Asif, U., Tang, J., Harrer, S.: Graspnet: An efficient convolutional neural network for real-time grasp detection for low-powered devices. In: International Joint Conferences on Artificial Intelligence (2018)
2018
Earlier work this paper cites.
Graham, B., Engelcke, M., Van Der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Suárez-Ruiz, F., Zhou, X., Pham, Q.C.: Can robots assemble an ikea chair? Science Robotics (2018)
2018
Earlier work this paper cites.
Ten Pas, A., Platt, R.: Using geometry to detect grasp poses in 3d point clouds. Robotics Research (2018)
2018
Earlier work this paper cites.
Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Liang, H., Ma, X., Li, S., Görner, M., Tang, S., Fang, B., Sun, F., Zhang, J.: Pointnetgpd: Detecting grasp configurations from point sets. In: International Conference on Robotics and Automation (2019)
2019
Cited alongside, same era.
Mousavian, A., Eppner, C., Fox, D.: 6-dof graspnet: Variational grasp generation for object manipulation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems (2019)
2019
Cited alongside, same era.
Yu, S., Zhai, D.H., Xia, Y.: Egnet: Efficient robotic grasp detection network. IEEE Transactions on Industrial Electronics (2022)
2022
Later among the works it cites.
Zheng, L., Cai, Y., Lu, T., Wang, S.: Vgpn: 6-dof grasp pose detection network based on hough voting. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2022)
2022
Later among the works it cites.
Zhou, H., Wang, X., Au, W., Kang, H., Chen, C.: Intelligent robots for fruit harvesting: Recent developments and future challenges. Precision Agriculture (2022)
2022
Later among the works it cites.
Chen, S., Tang, W., Xie, P., Yang, W., Wang, G.: Efficient heatmap-guided 6-dof grasp detection in cluttered scenes. IEEE Robotics and Automation Letters (2023)
2023
Later among the works it cites.
Chen, Z., Liu, Z., Xie, S., Zheng, W.S.: Grasp region exploration for 7-dof robotic grasping in cluttered scenes. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2023)
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Fang, H.S., Wang, C., Gou, M., Lu, C.: Graspnet-1billion: A large-scale benchmark for general object grasping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Ni, P., Zhang, W., Zhu, X., Cao, Q.: Pointnet++ grasping: Learning an end-to-end spatial grasp generation algorithm from sparse point clouds. In: IEEE International Conference on Robotics and Automation (2020)
2020
Cited alongside, same era.
Qin, Y., Chen, R., Zhu, H., Song, M., Xu, J., Su, H.: S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes. In: Conference on robot learning (2020)
2020
Cited alongside, same era.
Tang, Y., Ni, Z., Zhou, J., Zhang, D., Lu, J., Wu, Y., Zhou, J.: Uncertainty-aware score distribution learning for action quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Cited alongside, same era.
Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., Fu, Y.: Rethinking classification and localization for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Cited alongside, same era.
Breyer, M., Chung, J.J., Ott, L., Siegwart, R., Nieto, J.: Volumetric grasping network: Real-time 6 dof grasp detection in clutter. In: Conference on Robot Learning (2021)
2021
Cited alongside, same era.
Eppner, C., Mousavian, A., Fox, D.: Acronym: A large-scale grasp dataset based on simulation. In: IEEE International Conference on Robotics and Automation (2021)
2021
Cited alongside, same era.
2023
Later among the works it cites.
Dai, Q., Zhu, Y., Geng, Y., Ruan, C., Zhang, J., Wang, H.: Graspnerf: Multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf. In: IEEE International Conference on Robotics and Automation (2023)
2023
Later among the works it cites.
Fang, H.S., Wang, C., Fang, H., Gou, M., Liu, J., Yan, H., Liu, W., Xie, Y., Lu, C.: Anygrasp: Robust and efficient grasp perception in spatial and temporal domains. IEEE Transactions on Robotics (2023)
2023
Later among the works it cites.
Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., Zhu, S.C.: Diffusion-based generation, optimization, and planning in 3d scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Ma, H., Huang, D.: Towards scale balanced 6-dof grasp detection in cluttered scenes. In: Conference on Robot Learning (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, H., Niu, W., Zhuang, C.: Granet: A multi-level graph network for 6-dof grasp pose generation in cluttered scenes. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2023)
2023
Later among the works it cites.
Wang, R., Zhang, J., Chen, J., Xu, Y., Li, P., Liu, T., Wang, H.: Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation. In: IEEE International Conference on Robotics and Automation (2023)
2023
Later among the works it cites.
Weng, T., Held, D., Meier, F., Mukadam, M.: Neural grasp distance fields for robot manipulation. In: IEEE International Conference on Robotics and Automation (2023)
2023
Later among the works it cites.
Xu, Y., Wan, W., Zhang, J., Liu, H., Shan, Z., Shen, H., Wang, R., Geng, H., Weng, Y., Chen, J., et al.: Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Later among the works it cites.
Zhai, G., Huang, D., Wu, S.C., Jung, H., Di, Y., Manhardt, F., Tombari, F., Navab, N., Busam, B.: Monograspnet: 6-dof grasping with a single rgb image. In: IEEE International Conference on Robotics and Automation (2023)
2023
Later among the works it cites.
2024
Closest in time.
Liu, Z., Chen, Z., Zheng, W.S.: Simulating complete points representations for single-view 6-dof grasp detection. IEEE Robotics and Automation Letters (2024)
2024
Closest in time.
Wang, Y.K., Xing, C., Wei, Y.L., Wu, X.M., Zheng, W.S.: Single-view scene point cloud human grasp generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024)
2024
Closest in time.
2024
Closest in time.
Xu, G.H., Wei, Y.L., Zheng, D., Wu, X.M., Zheng, W.S.: Dexterous grasp transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024)
2024
Closest in time.