AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter

E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016

Original

2016

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 CVPR , 2020, pp. 11 444–11 453

2020

Earlier work this paper cites.

A. Murali, W. Liu et al. , “Same object, different grasps: Data and semantic knowledge for task-oriented grasping,” in CORL , 2021, pp. 1540–1557

2021

Earlier work this paper cites.

M. Caron, H. Touvron et al. , “Emerging properties in self-supervised vision transformers,” in ICCV , 2021, pp. 9650–9660

2021

Earlier work this paper cites.

H. Luo, W. Zhai et al. , “Learning affordance grounding from exocentric images,” in CVPR , 2022, pp. 2252–2261

2022

Earlier work this paper cites.

S. Bahl, R. Mendonca et al. , “Affordances from human videos as a versatile representation for robotics,” in CVPR , 2023, pp. 13 778–13 790

2023

Earlier work this paper cites.

C. Tang, D. Huang et al. , “Graspgpt: Leveraging semantic knowledge from a large language model for task-oriented grasping,” IEEE Robotics and Automation Letters , 2023

2023

Earlier work this paper cites.

P. Sun, S. Chen et al. , “Going denser with open-vocabulary part segmentation,” in ICCV , 2023, pp. 15 453–15 465

2023

Earlier work this paper cites.

J. Chen, D. Gao et al. , “Affordance grounding from demonstration video to target image,” in CVPR , 2023, pp. 6799–6808

2023

Earlier work this paper cites.

Y. Song, P. Sun et al. , “Learning 6-dof fine-grained grasp detection based on part affordance grounding,” arXiv preprint arXiv:2301.11564 , 2023

Original

2023

Earlier work this paper cites.

A. Rashid, S. Sharma et al. , “Language embedded radiance fields for zero-shot task-oriented grasping,” in 7th Annual Conference on Robot Learning , 2023

2023

Earlier work this paper cites.

J. Kerr, C. M. Kim et al. , “Lerf: Language embedded radiance fields,” in ICCV , 2023, pp. 19 729–19 739

2023

Cited alongside, same era.

H.-S. Fang, C. Wang et al. , “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , pp. 3929–3945, 2023

2023

Cited alongside, same era.

Y. Ju, K. Hu et al. , “Robo-abc: Affordance generalization beyond categories via semantic correspondence for robot manipulation,” in ECCV , 2024, pp. 222–239

2024

Cited alongside, same era.

Y. Kuang, J. Ye et al. , “Ram: Retrieval-based affordance transfer for generalizable zero-shot robotic manipulation,” arXiv preprint arXiv:2407.04689 , 2024

Original

2024

Cited alongside, same era.

W. Dong, D. Huang et al. , “Rtagrasp: Learning task-oriented grasping from human videos via retrieval, transfer, and alignment,” arXiv preprint arXiv:2409.16033 , 2024

S. Liu, Z. Zeng et al. , “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” in ECCV , 2024, pp. 38–55

2024

Later among the works it cites.

R. Mirjalili, M. Krawez, S. Silenzi, Y. Blei, and W. Burgard, “Lan-grasp: An effective approach to semantic object grasping using large language models,” in First Workshop on Vision-Language Models for Navigation and Manipulation at ICRA 2024

2024

Later among the works it cites.

E. Tong, A. Opipari, S. Lewis, Z. Zeng, and O. C. Jenkins, “Oval-prompt: Open-vocabulary affordance localization for robot manipulation through llm affordance-grounding,” arXiv preprint arXiv:2404.11000 , 2024

Original

2024

Later among the works it cites.

Y. Li, N. Zhao, J. Xiao et al. , “Laso: Language-guided affordance segmentation on 3d object,” in CVPR , 2024, pp. 14 251–14 260

2024

Later among the works it cites.