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We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds.
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The ycb object and model set: Towards common benchmarks for manipulation research
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Reinforcement learning of active vision for manipulating objects under occlusions
R. Cheng, A. Agarwal, and K. Fragkiadaki · 2018
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Point convolutional neural networks by extension operators
M. Atzmon, H. Maron, and Y. Lipman · 2018
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Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
N. Thomas, T. Smidt, S. Kearnes, L. Yang, L. Li, K. Kohlhoff, and P. Riley · 2018
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Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2019
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Pointpillars: Fast encoders for object detection from point clouds
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom · 2019
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C. R. Qi, O. Litany, K. He, and L. J. Guibas · 2019
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Densefusion: 6d object pose estimation by iterative dense fusion
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese · 2019
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Learning object bounding boxes for 3d instance segmentation on point clouds
B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, and N. Trigoni · 2019
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Gspn: Generative shape proposal network for 3d instance segmentation in point cloud
L. Yi, W. Zhao, H. Wang, M. Sung, and L. J. Guibas · 2019
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Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis
C. Chen, G. Li, R. Xu, T. Chen, M. Wang, and L. Lin · 2019
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Rotation invariant convolutions for 3d point clouds deep learning
Z. Zhang, B.-S. Hua, D. W. Rosen, and S.-K. Yeung · 2019
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Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation
Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun · 2020
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End-to-end multi-view fusion for 3d object detection in lidar point clouds
Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan · 2020
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The surprising effectiveness of representation learning for visual imitation
J. Pari, N. Muhammad, S. P. Arunachalam, L. Pinto, et al · 2021
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Learning visible connectivity dynamics for cloth smoothing
X. Lin, Y. Wang, Z. Huang, and D. Held · 2021
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox · 2021
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Robustness certification for point cloud models
T. Lorenz, A. Ruoss, M. Balunović, G. Singh, and M. Vechev · 2021
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A rotation-invariant framework for deep point cloud analysis
X. Li, R. Li, G. Chen, C.-W. Fu, D. Cohen-Or, and P.-A. Heng · 2021
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https://www.kinovarobotics.com , 2022
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V. E. Liong, T. N. T. Nguyen, S. Widjaja, D. Sharma, and Z. J. Chong · 2020
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S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency
M. Vecerik, J.-B. Regli, O. Sushkov, D. Barker, R. Pevceviciute, T. Rothörl, C. Schuster, R. Hadsell, L. Agapito, and J. Scholz · 2020
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Active perception and representation for robotic manipulation
Y. Zaky, G. Paruthi, B. Tripp, and J. Bergstra · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
D. Yarats, I. Kostrikov, and R. Fergus · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
F. Fuchs, D. Worrall, V. Fischer, and M. Welling · 2020
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Searching efficient 3d architectures with sparse point-voxel convolution
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han · 2020
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Generalization in dexterous manipulation via geometry-aware multi-task learning
W. Huang, I. Mordatch, P. Abbeel, and D. Pathak · 2021
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Kinova · 2022
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Vision-based manipulators need to also see from their hands
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Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation
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Benchmarking robustness of 3d point cloud recognition against common corruptions
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Ditto: Building digital twins of articulated objects from interaction
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You only demonstrate once: Category-level manipulation from single visual demonstration
B. Wen, W. Lian, K. Bekris, and S. Schaal · 2022
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Neural descriptor fields: Se(3)-equivariant object representations for manipulation
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