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Grasping is fundamental to robotic manipulation, and recent advances in large-scale grasping datasets have provided essential training data and evaluation benchmarks, accelerating the development of learning-based methods for robust object grasping.
H. D. Espinosa, P. D. Zavattieri, and G. L. Emore, “Adaptive fem computation of geometric and material nonlinearities with application to brittle failure,” Mechanics of Materials , vol. 29, no. 3-4, pp. 275–305, 1998
1998
Earlier work this paper cites.
A. T. Miller and P. K. Allen, “Graspit! a versatile simulator for robotic grasping,” IEEE Robotics & Automation Magazine , vol. 11, no. 4, pp. 110–122, 2004
2004
Earlier work this paper cites.
G. Irving, J. Teran, and R. Fedkiw, “Invertible finite elements for robust simulation of large deformation,” in Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation , 2004, pp. 131–140
2004
Earlier work this paper cites.
K. Yamane and Y. Nakamura, “A numerically robust lcp solver for simulating articulated rigid bodies in contact,” Proceedings of robotics: science and systems IV, Zurich, Switzerland , vol. 19, p. 20, 2008
2008
Earlier work this paper cites.
E. Drumwright and D. A. Shell, “A robust and tractable contact model for dynamic robotic simulation,” in Proceedings of the 2009 ACM symposium on Applied Computing , 2009, pp. 1176–1180
2009
Earlier work this paper cites.
M. Anitescu and A. Tasora, “An iterative approach for cone complementarity problems for nonsmooth dynamics,” Computational Optimization and Applications , vol. 47, pp. 207–235, 2010
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 5026–5033
2012
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, 2014
2014
Earlier work this paper cites.
M. A. Homel, R. M. Brannon, and J. Guilkey, “Controlling the onset of numerical fracture in parallelized implementations of the material point method (mpm) with convective particle domain interpolation (cpdi) domain scaling,” International Journal for Numerical Methods in Engineering , vol. 107, no. 1, pp. 31–48, 2016
2016
Earlier work this paper cites.
A. Ten Pas, M. Gualtieri, K. Saenko, and R. Platt, “Grasp pose detection in point clouds,” The International Journal of Robotics Research , vol. 36, no. 13-14, pp. 1455–1473, 2017
2017
Earlier work this paper cites.
F. Ficuciello, A. Migliozzi, E. Coevoet, A. Petit, and C. Duriez, “Fem-based deformation control for dexterous manipulation of 3d soft objects,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 4007–4013
2018
Earlier work this paper cites.
T. Wang, C. Yang, F. Kirchner, P. Du, F. Sun, and B. Fang, “Multimodal grasp data set: A novel visual–tactile data set for robotic manipulation,” International Journal of Advanced Robotic Systems , vol. 16, no. 1, p. 1729881418821571, 2019
2019
Earlier work this paper cites.
Y. Hu, J. Liu, A. Spielberg, J. B. Tenenbaum, W. T. Freeman, J. Wu, D. Rus, and W. Matusik, “Chainqueen: A real-time differentiable physical simulator for soft robotics,” in 2019 International conference on robotics and automation (ICRA) . IEEE, 2019, pp. 6265–6271
2019
Earlier work this paper cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453
2020
Earlier work this paper cites.
N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9917–9924
2020
Earlier work this paper cites.
M. Li, Z. Ferguson, T. Schneider, T. R. Langlois, D. Zorin, D. Panozzo, C. Jiang, and D. M. Kaufman, “Incremental potential contact: intersection-and inversion-free, large-deformation dynamics.” ACM Trans. Graph. , vol. 39, no. 4, p. 49, 2020
2020
Earlier work this paper 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, 2020
2020
Earlier work this paper cites.
C. Wu, J. Chen, Q. Cao, J. Zhang, Y. Tai, L. Sun, and K. Jia, “Grasp proposal networks: An end-to-end solution for visual learning of robotic grasps,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 174–13 184, 2020
2020
Earlier work this paper cites.
X. Wang, Y. Qiu, S. R. Slattery, Y. Fang, M. Li, S.-C. Zhu, Y. Zhu, M. Tang, D. Manocha, and C. Jiang, “A massively parallel and scalable multi-gpu material point method,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 30–1, 2020
2020
Cited alongside, same era.
Y. Hu, T. Schneider, B. Wang, D. Zorin, and D. Panozzo, “Fast tetrahedral meshing in the wild,” ACM Transactions on Graphics (ToG) , vol. 39, no. 4, pp. 117–1, 2020
2020
Cited alongside, same era.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang et al. , “Sapien: A simulated part-based interactive environment,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 097–11 107
2020
Cited alongside, same era.
2021
Cited alongside, same era.
P. Li, T. Liu, Y. Li, Y. Geng, Y. Zhu, Y. Yang, and S. Huang, “Gendexgrasp: Generalizable dexterous grasping,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8068–8074
2023
Later among the works it cites.
Y. Xu, W. Wan, J. Zhang, H. Liu, Z. Shan, H. Shen, R. Wang, H. Geng, Y. Weng, J. Chen 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, pp. 4737–4746
2023
Later among the works it cites.
X. Yu, S. Zhao, S. Luo, G. Yang, and L. Shao, “Diffclothai: Differentiable cloth simulation with intersection-free frictional contact and differentiable two-way coupling with articulated rigid bodies,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 400–407
2023
Later among the works it cites.
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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) . IEEE, 2021, pp. 6222–6227
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Liu, Z. Liu, Z. Jiao, Y. Zhu, and S.-C. Zhu, “Synthesizing diverse and physically stable grasps with arbitrary hand structures using differentiable force closure estimator,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 470–477, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
L. Lan, D. M. Kaufman, M. Li, C. Jiang, and Y. Yang, “Affine body dynamics: fast, stable and intersection-free simulation of stiff materials,” ACM Transactions on Graphics (TOG) , vol. 41, no. 4, pp. 1–14, 2022
2022
Cited alongside, same era.
Y. Chen, M. Li, L. Lan, H. Su, Y. Yang, and C. Jiang, “A unified newton barrier method for multibody dynamics,” ACM Transactions on Graphics (TOG) , vol. 41, no. 4, pp. 1–14, 2022
2022
Cited alongside, same era.
H. Zhang, D. Yang, H. Wang, B. Zhao, X. Lan, J. Ding, and N. Zheng, “Regrad: A large-scale relational grasp dataset for safe and object-specific robotic grasping in clutter,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2929–2936, 2022
2022
Cited alongside, same era.
2024
Later among the works it cites.
W. Du, S. Yao, X. Wang, Y. Xu, W. Xu, and C. Lu, “Intersection-free robot manipulation with soft-rigid coupled incremental potential contact,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
A. D. Vuong, M. N. Vu, H. Le, B. Huang, H. T. T. Binh, T. Vo, A. Kugi, and A. Nguyen, “Grasp-anything: Large-scale grasp dataset from foundation models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 14 030–14 037
2024
Later among the works it cites.
H. Zhang, S. Christen, Z. Fan, O. Hilliges, and J. Song, “Graspxl: Generating grasping motions for diverse objects at scale,” in European Conference on Computer Vision . Springer, 2024, pp. 386–403
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Shao and C. Xiao, “Bimanual grasp synthesis for dexterous robot hands,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
W. Xu, J. Zhang, T. Tang, Z. Yu, Y. Li, and C. Lu, “Dipgrasp: Parallel local searching for efficient differentiable grasp planning,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
J. Lu, H. Kang, H. Li, B. Liu, Y. Yang, Q. Huang, and G. Hua, “Ugg: Unified generative grasping,” in European Conference on Computer Vision . Springer, 2024, pp. 414–433
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Zong, C. Jiang, and X. Han, “A convex formulation of frictional contact for the material point method and rigid bodies,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 1831–1838
2024
Later among the works it cites.
M. Li, C. Jiang, and Z. Luo, Physics-Based Simulation , 2024. [Online]. Available: https://phys-sim-book.github.io/
2024
Later among the works it cites.
W. Du, W. Xu, J. Ren, Z. Yu, and C. Lu, “Tacipc: Intersection-and inversion-free fem-based elastomer simulation for optical tactile sensors,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 2559–2566, 2024
2024
Later among the works it cites.
J. A. Fernández-Fernández, R. Lange, S. Laible, K. O. Arras, and J. Bender, “Stark: A unified framework for strongly coupled simulation of rigid and deformable bodies with frictional contact,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 16 888–16 894
2024
Later among the works it cites.
M. Saleh, M. Sommersperger, N. Navab, and F. Tombari, “Physics-encoded graph neural networks for deformation prediction under contact,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 17 160–17 166
2024
Later among the works it cites.