[50] Lawrence Smith, Travis Hainsworth, Jacob Haimes, and Robert MacCurdy. Automated synthesis of bending pneumatic soft actuators. In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), pages 358-363. IEEE, 2022.
[51] Lawrence Smith and Robert MacCurdy. Soroforge: End-to-end soft actuator design. IEEE Transactions on Automation Science and Engineering, 2023.
[52] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[54] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
[55] Andrew Spielberg, Alexander Amini, Lillian Chin, Wojciech Matusik, and Daniela Rus. Co-learning of task and sensor placement for soft robotics. IEEE Robotics and Automation Letters, 6(2):1208-1215, 2021.
[56] Andrew Spielberg, Tao Du, Yuanming Hu, Daniela Rus, and Wojciech Matusik. Advanced soft robot modeling in chainqueen. Robotica, 41(1):74-104, 2023.
[57] Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, and Daniela Rus. Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations. Advances in Neural Information Processing Systems, 32, 2019.
[58] Kenneth O Stanley. Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, 8:131-162, 2007.
[59] Michael T Tolley, Robert F Shepherd, Michael Karpelson, Nicholas W Bartlett, Kevin C Galloway, Michael Wehner, Rui Nunes, George M Whitesides, and Robert J Wood. An untethered jumping soft robot. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 561-566. IEEE, 2014.
[60] Merel Van Diepen and Kristina Shea. A spatial grammar method for the computational design synthesis of virtual soft locomotion robots. Journal of Mechanical Design, 141(10), 2019.
[61] Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, and Chuang Gan. Softzoo: A soft robot co-design benchmark for locomotion in diverse environments. In The Eleventh International Conference on Learning Representations, 2023.
[62] Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S Jaakkola. Crystal diffusion variational autoencoder for periodic material generation. In International Conference on Learning Representations, 2021.
[63] Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, , and Jian Tang. GeoDiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations, 2021.
[64] Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. Physdiff: Physics-guided human motion diffusion model. arXiv preprint arXiv:2212.02500, 2022.
[65] Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. Lion: Latent point diffusion models for 3d shape generation. arXiv preprint arXiv:2210.06978, 2022.
[66] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5826-5835, 2021.