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Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments.
Determining optical flow
B. K. P. Horn and B. G. Schunck · 1981
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J.-S. Cheong, A. F. Van Der Stappen, K. Goldberg, M. H. Overmars, and E. Rimon · 2007
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Learning to manipulate articulated objects in unstructured environments using a grounded relational representation
D. Katz, Y. Pyuro, and O. Brock · 2008
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S. Chitta, B. Cohen, and M. Likhachev · 2010
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D. Berenson, S. Srinivasa, and J. Kuffner · 2011
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F. Burget, A. Hornung, and M. Bennewitz · 2013
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D. P. Kingma and J. Ba · 2014
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V. Narayanan and M. Likhachev · 2015
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FlowNet: Learning optical flow with convolutional networks, 2015
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A. Amiranashvili, A. Dosovitskiy, V. Koltun, and T. Brox · 2018
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Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding
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Avplug: Approach vector planning for unicontact grasping amid clutter
Y. Avigal, V. Satish, Z. Tam, H. Huang, H. Zhang, M. Danielczuk, J. Ichnowski, and K. Goldberg · 2021
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Planar robot casting with real2sim2real self-supervised learning
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N. Nie, S. Y. Gadre, K. Ehsani, and S. Song · 2022
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B. Eisner, H. Zhang, and D. Held · 2022
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Z. Yan, R. Hu, X. Yan, L. Chen, O. Van Kaick, H. Zhang, and H. Huang · 2020
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Sapien: A simulated part-based interactive environment
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, and Others · 2020
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6-dof grasp planning using fast 3d reconstruction and grasp quality cnn
Y. Avigal, S. Paradis, and H. Zhang · 2020
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Category-Level articulated object pose estimation, 2020
X. Li, H. Wang, L. Yi, L. J. Guibas, A. Lynn Abbott, and S. Song · 2020
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Dex-Net AR: Distributed deep grasp planning using a commodity cellphone and augmented reality app
H. Zhang, J. Ichnowski, Y. Avigal, J. Gonzales, I. Stoica, and K. Goldberg · 2020
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Visual identification of articulated object parts
V. Zeng, T. E. Lee, J. Liang, and O. Kroemer · 2020
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RAFT: Recurrent All-Pairs field transforms for optical flow
Z. Teed and J. Deng · 2020
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Umpnet: Universal manipulation policy network for articulated objects
Z. Xu, H. Zhanpeng, and S. Song · 2022
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Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting
V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg · 2022
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Fabricflownet: Bimanual cloth manipulation with a flow-based policy
T. Weng, S. M. Bajracharya, Y. Wang, K. Agrawal, and D. Held · 2022
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A. Elmquist, A. Young, T. Hansen, S. Ashokkumar, S. Caldararu, A. Dashora, I. Mahajan, H. Zhang, L. Fang, H. Shen, et al · 2022
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Tax-pose: Task-specific cross-pose estimation for robot manipulation
C. Pan, B. Okorn, H. Zhang, B. Eisner, and D. Held · 2023
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Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
D. Seita, Y. Wang, S. J. Shetty, E. Y. Li, Z. Erickson, and D. Held · 2023
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Flowbot++: Learning generalized articulated objects manipulation via articulation projection
H. Zhang, B. Eisner, and D. Held · 2023
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Apla: Additional perturbation for latent noise with adversarial training enables consistency
Y. Yao, S. Deng, Z. Cao, H. Zhang, and L.-J. Deng · 2023
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