[37] Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6), November 2017.
[38] Silvia Zuffi, Angjoo Kanazawa, David Jacobs, and Michael J. Black. 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), July 2017.
[39] Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, et al. Sapien: A simulated part-based interactive environment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11097-11107, 2020.
[40] Anthony Dearden and Yiannis Demiris. Learning forward models for robots. In *IJCAI*, volume 5, page 1440, 2005.
[41] Jürgen Sturm, Christian Plagemann, and Wolfram Burgard. Adaptive body scheme models for robust robotic manipulation. In Robotics: Science and systems. Zurich, 2008.
[42] Jurgen Sturm, Christian Plagemann, and Wolfram Burgard. Unsupervised body scheme learning through self-perception. In 2008 IEEE International Conference on Robotics and Automation, pages 3328-3333. IEEE, 2008.
[43] Jürgen Sturm, Vijay Pradeep, Cyrill Stachniss, Christian Plagemann, Kurt Konolige, and Wolfram Burgard. Learning kinematic models for articulated objects. In Twenty-First International Joint Conference on Artificial Intelligence. CiteSeer, 2009.
[44] Jürgen Sturm, Cyril Stachniss, and Wolfram Burgard. A probabilistic framework for learning kinematic models of articulated objects. Journal of Artificial Intelligence Research, 41:477-526, 2011.
[45] Neil Nie, Samir Yitzhak Gadre, Kiana Ehsani, and Shuran Song. Structure from action: Learning interactions for articulated object 3d structure discovery. arXiv preprint arXiv:2207.08997, 2022.
[46] Dov Katz and Oliver Brock. Manipulating articulated objects with interactive perception. In 2008 IEEE International Conference on Robotics and Automation, pages 272-277. IEEE, 2008.
[47] Roberto Martin Martin and Oliver Brock. Online interactive perception of articulated objects with multilevel recursive estimation based on task-specific priors. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2494-2501. IEEE, 2014.
[48] Karol Hausman, Scott Niekum, Sarah Osentoski, and Gaurav S Sukhatme. Active articulation model estimation through interactive perception. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 3305-3312. IEEE, 2015.
[49] Roberto Martin-Martin, Sebastian Hofer, and Oliver Brock. An integrated approach to visual perception of articulated objects. In 2016 IEEE international conference on robotics and automation (ICRA), pages 5091-5097. IEEE, 2016.
[50] Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, and Gaurav S Sukhatme. Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6):1273-1291, 2017.
[51] Cheng-Chun Hsu, Zhenyu Jiang, and Yuke Zhu. Ditto in the house: Building articulation models of indoor scenes through interactive perception. arXiv preprint arXiv:2302.01295, 2023.
[52] Ruizhen Hu, Wenchao Li, Oliver Van Kaick, Ariel Shamir, Hao Zhang, and Hui Huang. Learning to predict part mobility from a single static snapshot. ACM Transactions on Graphics (TOG), 36(6):1-13, 2017.
[53] Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, and Leonidas Guibas. Deep part induction from articulated object pairs. arXiv preprint arXiv:1809.07417, 2018.
[54] Ben Abbatematteo, Stefanie Telsex, and George Konidaris. Learning to generalize kinematic models to novel objects. In Proceedings of the 3rd Conference on Robot Learning, 2019.
[55] 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.