Fetching the paper…
Reading the bibliography…
Articulated objects like cabinets and doors are widespread in daily life.
H. W. Kuhn, “The hungarian method for the assignment problem,”
1955
Earlier work this paper cites.
J. J. Gibson, “The theory of affordances,”
1977
Earlier work this paper cites.
M. Ester, H.-P. Kriegel, J. Sander, X. Xu
1996
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in
2015
Earlier work this paper cites.
Y. Karayiannidis, C. Smith, F. E. V. Barrientos, P. Ögren, and D. Kragic, “An adaptive control approach for opening doors and drawers under uncertainties,”
2016
Earlier work this paper cites.
E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri, “3d shape segmentation with projective convolutional networks,” in
2017
Earlier work this paper cites.
L. Yi, H. Su, X. Guo, and L. J. Guibas, “Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation,” in
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,”
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in
2017
Earlier work this paper cites.
K. Fang, T.-L. Wu, D. Yang, S. Savarese, and J. J. Lim, “Demo2vec: Reasoning object affordances from online videos,” in
2018
Earlier work this paper cites.
S. Fujimoto, H. Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in
2018
Earlier work this paper cites.
X. Wang, B. Zhou, Y. Shi, X. Chen, Q. Zhao, and K. Xu, “Shape2motion: Joint analysis of motion parts and attributes from 3d shapes,” in
2019
Earlier work this paper cites.
B. Abbatematteo, S. Tellex, and G. Konidaris, “Learning to generalize kinematic models to novel objects,” in
2019
Earlier work this paper cites.
X. Li, H. Wang, L. Yi, L. J. Guibas, A. L. Abbott, and S. Song, “Category-level articulated object pose estimation,” in
2020
Earlier work this paper cites.
L. P. Kaelbling, “The foundation of efficient robot learning,”
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
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
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Qin, R. Chen, H. Zhu, M. Song, J. Xu, and H. Su, “S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes,” in
2020
Cited alongside, same era.
M. Kokic, D. Kragic, and J. Bohg, “Learning task-oriented grasping from human activity datasets,”
2020
Cited alongside, same era.
A. Jain, R. Lioutikov, C. Chuck, and S. Niekum, “Screwnet: Category-independent articulation model estimation from depth images using screw theory,” in
2021
Later among the works it cites.
L. Liu, H. Xue, W. Xu, H. Fu, and C. Lu, “Toward real-world category-level articulation pose estimation,”
2022
Later among the works it cites.
Z. Jiang, C.-C. Hsu, and Y. Zhu, “Ditto: Building digital twins of articulated objects from interaction,” in
2022
Later among the works it cites.
J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, Y. Tang, S. Tao, X. Wei, Y. Yao
2022
Later among the works it cites.
H. Shen, W. Wan, and H. Wang, “Learning category-level generalizable object manipulation policy via generative adversarial self-imitation learning from demonstrations,”
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Fang, Y. Zhu, A. Garg, A. Kurenkov, V. Mehta, L. Fei-Fei, and S. Savarese, “Learning task-oriented grasping for tool manipulation from simulated self-supervision,”
2020
Cited alongside, same era.
T. Nagarajan and K. Grauman, “Learning affordance landscapes for interaction exploration in 3d environments,”
2020
Cited alongside, same era.
T. Nagarajan, Y. Li, C. Feichtenhofer, and K. Grauman, “Ego-topo: Environment affordances from egocentric video,” in
2020
Cited alongside, same era.
S. Y. Gadre, K. Ehsani, and S. Song, “Act the part: Learning interaction strategies for articulated object part discovery,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
P. Mandikal and K. Grauman, “Learning dexterous grasping with object-centric visual affordances,” in
2021
Cited alongside, same era.
D. Xu, A. Mandlekar, R. Martín-Martín, Y. Zhu, S. Savarese, and L. Fei-Fei, “Deep affordance foresight: Planning through what can be done in the future,” in
2021
Cited alongside, same era.
L. Liu, W. Xu, H. Fu, S. Qian, Q. Yu, Y. Han, and C. Lu, “Akb-48: a real-world articulated object knowledge base,” in
2022
Later among the works it cites.
X. Lai, J. Liu, L. Jiang, L. Wang, H. Zhao, S. Liu, X. Qi, and J. Jia, “Stratified transformer for 3d point cloud segmentation,” in
2022
Later among the works it cites.
Y. Wang, R. Wu, K. Mo, J. Ke, Q. Fan, L. J. Guibas, and H. Dong, “Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions,” in
2022
Later among the works it cites.
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in
2022
Later among the works it cites.
M. Mittal, D. Hoeller, F. Farshidian, M. Hutter, and A. Garg, “Articulated object interaction in unknown scenes with whole-body mobile manipulation,” in
2022
Later among the works it cites.
H. Geng, H. Xu, C. Zhao, C. Xu, L. Yi, S. Huang, and H. Wang, “Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts,” in
2023
Closest in time.
J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, Y. Tang, S. Tao, X. Wei, Y. Yao
2023
Closest in time.
P.-L. Guhur, S. Chen, R. G. Pinel, M. Tapaswi, I. Laptev, and C. Schmid, “Instruction-driven history-aware policies for robotic manipulations,” in
2023
Closest in time.