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This work tackles the problem of task-oriented dexterous hand pose synthesis, which involves generating a static hand pose capable of applying a task-specific set of wrenches to manipulate objects.
Z. Li and S. S. Sastry, “Task-oriented optimal grasping by multifingered robot hands,” IEEE Journal on Robotics and Automation , vol. 4, no. 1, pp. 32–44, 1988
1988
Earlier work this paper cites.
C. Ferrari, J. F. Canny, et al. , “Planning optimal grasps.” in ICRA , vol. 3, no. 4, 1992, p. 6
1992
Earlier work this paper cites.
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. IEEE, 2004, pp. 319–325
2004
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.
S. R. Lay, Convex sets and their applications . Courier Corporation, 2007
2007
Earlier work this paper cites.
Y. Zheng and W.-H. Qian, “Improving grasp quality evaluation,” Robotics and Autonomous Systems , vol. 57, no. 6-7, pp. 665–673, 2009
2009
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H. Kruger and A. F. van der Stappen, “Partial closure grasps: Metrics and computation,” in 2011 IEEE International Conference on Robotics and Automation . IEEE, 2011, pp. 5024–5030
2011
Earlier work this paper cites.
H. Kruger, E. Rimon, and A. F. van der Stappen, “Local force closure,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 4176–4182
2012
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Y. Zheng, “An efficient algorithm for a grasp quality measure,” IEEE Transactions on Robotics , vol. 29, no. 2, pp. 579–585, 2012
2012
Earlier work this paper cites.
A. Sahbani, S. El-Khoury, and P. Bidaud, “An overview of 3d object grasp synthesis algorithms,” Robotics and Autonomous Systems , vol. 60, no. 3, pp. 326–336, 2012
2012
Earlier work this paper cites.
J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,” IEEE Transactions on robotics , vol. 30, no. 2, pp. 289–309, 2013
2013
Earlier work this paper cites.
A. Domahidi, E. Chu, and S. Boyd, “ECOS: An SOCP solver for embedded systems,” in European Control Conference (ECC) , 2013, pp. 3071–3076
2013
Earlier work this paper cites.
S. Kim, A. Shukla, and A. Billard, “Catching objects in flight,” IEEE Transactions on Robotics , vol. 30, no. 5, pp. 1049–1065, 2014
2014
Earlier work this paper cites.
S. El-Khoury, R. De Souza, and A. Billard, “On computing task-oriented grasps,” Robotics and Autonomous Systems , vol. 66, pp. 145–158, 2015
2015
Earlier work this paper cites.
Y. Lin and Y. Sun, “Grasp planning to maximize task coverage,” The International Journal of Robotics Research , vol. 34, no. 9, pp. 1195–1210, 2015
2015
Earlier work this paper cites.
M. A. Roa and R. Suárez, “Grasp quality measures: review and performance,” Autonomous robots , vol. 38, pp. 65–88, 2015
2015
Cited alongside, same era.
R. Krug, Y. Bekiroglu, and M. A. Roa, “Grasp quality evaluation done right: How assumed contact force bounds affect wrench-based quality metrics,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 1595–1600
2017
Cited alongside, same era.
E. Arruda, M. J. Mathew, M. Kopicki, M. Mistry, M. Azad, and J. L. Wyatt, “Uncertainty averse pushing with model predictive path integral control,” in 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) . IEEE, 2017, pp. 497–502
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Qiu and M. R. Kermani, “A new approach for grasp quality calculation using continuous boundary formulation of grasp wrench space,” Mechanism and Machine Theory , vol. 168, p. 104524, 2022
2022
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
Closest in time.
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
Closest in time.
2023
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H. Dai, A. Majumdar, and R. Tedrake, “Synthesis and optimization of force closure grasps via sequential semidefinite programming,” Robotics Research: Volume 1 , pp. 285–305, 2018
2018
Cited alongside, same era.
L. Shao, F. Ferreira, M. Jorda, V. Nambiar, J. Luo, E. Solowjow, J. A. Ojea, O. Khatib, and J. Bohg, “Unigrasp: Learning a unified model to grasp with multifingered robotic hands,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2286–2293, 2020
2020
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.
H. Jiang, S. Liu, J. Wang, and X. Wang, “Hand-object contact consistency reasoning for human grasps generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 107–11 116
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. Turpin, L. Wang, E. Heiden, Y.-C. Chen, M. Macklin, S. Tsogkas, S. Dickinson, and A. Garg, “Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands,” in European Conference on Computer Vision . Springer, 2022, pp. 201–221
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Wei, D. Li, P. Wang, Y. Li, W. Li, Y. Luo, and J. Zhong, “Dvgg: Deep variational grasp generation for dextrous manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1659–1666, 2022
2022
Cited alongside, same era.
Closest in time.
R. Wang, J. Zhang, J. Chen, Y. Xu, P. Li, T. Liu, and H. Wang, “Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 359–11 366
2023
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B. Sundaralingam, S. K. S. Hari, A. Fishman, C. Garrett, K. V. Wyk, V. Blukis, A. Millane, H. Oleynikova, A. Handa, F. Ramos, N. Ratliff, and D. Fox, “curobo: Parallelized collision-free minimum-jerk robot motion generation,” 2023
2023
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2023
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S. Chen, A. Wu, and C. K. Liu, “Synthesizing dexterous nonprehensile pregrasp for ungraspable objects,” in ACM SIGGRAPH 2023 Conference Proceedings , 2023, pp. 1–10
2023
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T. Chen, M. Tippur, S. Wu, V. Kumar, E. Adelson, and P. Agrawal, “Visual dexterity: In-hand reorientation of novel and complex object shapes,” Science Robotics , vol. 8, no. 84, p. eadc9244, 2023
2023
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X. Cheng, S. Patil, Z. Temel, O. Kroemer, and M. T. Mason, “Enhancing dexterity in robotic manipulation via hierarchical contact exploration,” IEEE Robotics and Automation Letters , vol. 9, no. 1, pp. 390–397, 2023
2023
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T. Pang, H. T. Suh, L. Yang, and R. Tedrake, “Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models,” IEEE Transactions on Robotics , 2023
2023
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