Fetching the paper…
Reading the bibliography…
Robotic grasping is one of the most fundamental tasks in robotic manipulation, and grasp detection/generation has long been the subject of extensive research.
Y. Jiang, S. Moseson, and A. Saxena, “Efficient grasping from rgbd images: Learning using a new rectangle representation,” in 2011 IEEE International conference on robotics and automation . IEEE, 2011, pp. 3304–3311
2011
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 1316–1322
2015
Earlier work this paper cites.
S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 769–776
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Morrison, P. Corke, and J. Leitner, “Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach,” Robotics: Science and Systems XIV , pp. 1–10, 2018
2018
Earlier work this paper cites.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 2901–2910
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Kumra, S. Joshi, and F. Sahin, “Antipodal robotic grasping using generative residual convolutional neural network,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9626–9633
2020
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.
S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He, “Zero: Memory optimizations toward training trillion parameter models,” in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis . IEEE, 2020, pp. 1–16
2020
Earlier work this paper cites.
S. Ainetter and F. Fraundorfer, “End-to-end trainable deep neural network for robotic grasp detection and semantic segmentation from rgb,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 452–13 458
2021
Earlier work this paper 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) . IEEE, 2021, pp. 13 438–13 444
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PmLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
Y. Lu, B. Deng, Z. Wang, P. Zhi, Y. Li, and S. Wang, “Hybrid physical metric for 6-dof grasp pose detection,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8238–8244
2022
Earlier work this paper cites.
K. Xu, S. Zhao, Z. Zhou, Z. Li, H. Pi, Y. Zhu, Y. Wang, and R. Xiong, “A joint modeling of vision-language-action for target-oriented grasping in clutter,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 597–11 604
2023
Cited alongside, same era.
C. Tang, D. Huang, W. Ge, W. Liu, and H. Zhang, “Graspgpt: Leveraging semantic knowledge from a large language model for task-oriented grasping,” IEEE Robotics and Automation Letters , vol. 8, no. 11, pp. 7551–7558, 2023
2023
Cited alongside, same era.
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3929–3945, 2023
2023
Cited alongside, same era.
Y. Lu, Y. Fan, B. Deng, F. Liu, Y. Li, and S. Wang, “Vl-grasp: a 6-dof interactive grasp policy for language-oriented objects in cluttered indoor scenes,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 976–983
H. Shao, S. Qian, H. Xiao, G. Song, Z. Zong, L. Wang, Y. Liu, and H. Li, “Visual cot: Advancing multi-modal language models with a comprehensive dataset and benchmark for chain-of-thought reasoning,” Advances in Neural Information Processing Systems , vol. 37, pp. 8612–8642, 2024
2024
Later among the works it cites.
J. Zheng, J. Li, S. Cheng, Y. Zheng, J. Li, J. Liu, Y. Liu, J. Liu, and X. Zhan, “Instruction-guided visual masking,” Advances in neural information processing systems , vol. 37, pp. 126 004–126 031, 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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic, et al. , “Deep learning approaches to grasp synthesis: A review,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3994–4015, 2023
2023
Cited alongside, same era.
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 11 975–11 986
2023
Cited alongside, same era.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 4195–4205
2023
Cited alongside, same era.
C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , p. 02783649241273668, 2023
2023
Cited alongside, same era.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, pp. 34 892–34 916, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
T. Nguyen, M. N. Vu, B. Huang, A. Vuong, Q. Vuong, N. Le, T. Vo, and A. Nguyen, “Language-driven 6-dof grasp detection using negative prompt guidance,” in European Conference on Computer Vision . Springer, 2024, pp. 363–381
2024
Cited alongside, same era.
J. Xu, S. Jin, Y. Lei, Y. Zhang, and L. Zhang, “Rt-grasp: Reasoning tuning robotic grasping via multi-modal large language model,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7323–7330
2024
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
T. Cheng, L. Song, Y. Ge, W. Liu, X. Wang, and Y. Shan, “Yolo-world: Real-time open-vocabulary object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 16 901–16 911
2024
Later among the works it cites.
Q. Fan, Y. Cai, C. Li, C. Jiao, X. Zheng, T. Lu, B. Liang, and S. Wang, “Miscgrasp: Leveraging multiple integrated scales and contrastive learning for enhanced volumetric grasping,” 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2025
2025
Closest in time.
S. Jin, J. XU, Y. Lei, and L. Zhang, “Reasoning grasping via multimodal large language model,” in Conference on Robot Learning . PMLR, 2025, pp. 3809–3827
2025
Closest in time.
Y. Qian, X. Zhu, O. Biza, S. Jiang, L. Zhao, H. Huang, Y. Qi, and R. Platt, “Thinkgrasp: A vision-language system for strategic part grasping in clutter,” in Conference on Robot Learning . PMLR, 2025, pp. 3568–3586
2025
Closest in time.
G. Tziafas and H. Kasaei, “Towards open-world grasping with large vision-language models,” in Conference on Robot Learning . PMLR, 2025, pp. 3304–3332
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.