[16] Mia Kokic, Danica Kragic, and Jeannette Bohg. Learning task-oriented grasping from human activity datasets. IEEE Robotics and Automation Letters, 5(2):3352-3359, 2020.
[17] Mia Kokic, Johannes A. Stork, Joshua A. Haustein, and Danica Kragic. Affordance detection for task-specific grasping using deep learning. In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages 91-98, 2017.
[18] Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, and Alan Yuille. Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion. International Journal of Computer Vision, 129:736-760, 2021.
[19] James J Kuffner and Steven M LaValle. Rrt-connect: An efficient approach to single-query path planning. In Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), volume 2, pages 995-1001. IEEE, 2000.
[20] Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, Marcus Dominguez-Kuhn, Animesh Garg, Roberto Martin-Martin, and Silvio Savarese. Visuomotor mechanical search: Learning to retrieve target objects in clutter. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8408-8414. IEEE, 2020.
[21] Jinhwi Lee, Younggil Cho, Changjoo Nam, Jonghyeon Park, and Changhwan Kim. Efficient obstacle rearrangement for object manipulation tasks in cluttered environments. In 2019 International Conference on Robotics and Automation (ICRA), pages 183-189. IEEE, 2019.
[22] Xiaolong Li, He Wang, Li Yi, Leonidas J Guibas, A Lynn Abbott, and Shuran Song. Category-level articulated object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3706-3715, 2020.
[23] Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory D Hager, and Alan L Yuille. Nothing but geometric constraints: A model-free method for articulated object pose estimation. arXiv preprint arXiv:2012.00088, 2020.
[24] Priyanka Mandikal and Kristen Grauman. Learning dexterous grasping with object-centric visual affordances. In IEEE International Conference on Robotics and Automation (ICRA), 2021.
[25] Kaichun Mo, Leonidas Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In International Conference on Computer Vision (ICCV), 2021.
[26] Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, and Leonidas Guibas. O2O-Afford: Annotation-free large-scale object-object affordance learning. In Conference on Robot Learning (CoRL), 2021.
[27] Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su. PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[28] Luis Montesano and Manuel Lopes. Learning grasping affordances from local visual descriptors. In 2009 IEEE 8th international conference on development and learning, pages 1-6. IEEE, 2009.
[29] Chuanruo Ning, Ruihai Wu, Haoran Lu, Kaichun Mo, and Hao Dong. Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects. In Advances in Neural Information Processing Systems (NeurIPS), 2023.
[30] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv preprint arXiv:1612.00593, 2016.
[31] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.