Fetching the paper…
Reading the bibliography…
Research in manipulation of deformable objects is typically conducted on a limited range of scenarios, because handling each scenario on hardware takes significant effort.
W. Sun, M. Çetin, R. Chan, and A. S. Willsky, “Learning the dynamics and time-recursive boundary detection of deformable objects,” IEEE Transactions on Image Processing , vol. 17, no. 11, pp. 2186–2200, 2008
2008
Earlier work this paper cites.
H. Wang, J. F. O’Brien, and R. Ramamoorthi, “Data-driven elastic models for cloth: modeling and measurement,” ACM transactions on graphics (TOG) , vol. 30, no. 4, pp. 1–12, 2011
2011
Earlier work this paper cites.
R. Narain, A. Samii, and J. F. O’brien, “Adaptive anisotropic remeshing for cloth simulation,” ACM transactions on graphics (TOG) , vol. 31, no. 6, pp. 1–10, 2012. [Online]. Available: graphics.berkeley.edu/resources/ARCSim
2012
Earlier work this paper cites.
D. Navarro-Alarcon, Y.-H. Liu, J. G. Romero, and P. Li, “Model-free visually servoed deformation control of elastic objects by robot manipulators,” IEEE Transactions on Robotics , vol. 29, no. 6, pp. 1457–1468, 2013
2013
Earlier work this paper cites.
E. Yoshida, K. Ayusawa, I. G. Ramirez-Alpizar, K. Harada, C. Duriez, and A. Kheddar, “Simulation-based optimal motion planning for deformable object,” in 2015 IEEE international workshop on advanced robotics and its social impacts (ARSO) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
S. D. Klee, B. Q. Ferreira, R. Silva, J. P. Costeira, F. S. Melo, and M. Veloso, “Personalized assistance for dressing users,” in International Conference on Social Robotics . Springer, 2015, pp. 359–369
2015
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
S. Yang, J. Liang, and M. C. Lin, “Learning-based cloth material recovery from video,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 4383–4393
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik, “Multi-view supervision for single-view reconstruction via differentiable ray consistency,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2626–2634
2017
Earlier work this paper cites.
J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Conference on Robot Learning . PMLR, 2018, pp. 734–743
2018
Earlier work this paper cites.
A. Clegg, W. Yu, J. Tan, C. K. Liu, and G. Turk, “Learning to dress: Synthesizing human dressing motion via deep reinforcement learning,” ACM Transactions on Graphics (TOG) , vol. 37, no. 6, pp. 1–10, 2018
2018
Earlier work this paper cites.
T. Tang, C. Wang, and M. Tomizuka, “A framework for manipulating deformable linear objects by coherent point drift,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3426–3433, 2018
2018
Earlier work this paper cites.
J. Zhang, L. Tai, P. Yun, Y. Xiong, M. Liu, J. Boedecker, and W. Burgard, “Vr-goggles for robots: Real-to-sim domain adaptation for visual control,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1148–1155, 2019
2019
Earlier work this paper cites.
X. Liu, M. Yan, and J. Bohg, “Meteornet: Deep learning on dynamic 3d point cloud sequences,” in ICCV , 2019
2019
Earlier work this paper cites.
A. Kapusta, Z. Erickson, H. M. Clever, W. Yu, C. K. Liu, G. Turk, and C. C. Kemp, “Personalized collaborative plans for robot-assisted dressing via optimization and simulation,” Autonomous Robots , vol. 43, no. 8, pp. 2183–2207, 2019
2019
Earlier work this paper cites.
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox, “Closing the sim-to-real loop: Adapting simulation randomization with real world experience,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8973–8979
2019
Cited alongside, same era.
F. Ramos, R. C. Possas, and D. Fox, “BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators,” in Robotics: Science and Systems (RSS) , 2019
2019
Cited alongside, same era.
R. Herguedas, G. López-Nicolás, R. Aragüés, and C. Sagüés, “Survey on multi-robot manipulation of deformable objects,” in IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) , 2019
2019
Cited alongside, same era.
W. Chen, H. Ling, J. Gao, E. Smith, J. Lehtinen, A. Jacobson, and S. Fidler, “Learning to predict 3d objects with an interpolation-based differentiable renderer,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in European conference on computer vision . Springer, 2020, pp. 405–421
2020
Later among the works it cites.
2020
Later among the works it cites.
Y.-L. Qiao, J. Liang, V. Koltun, and M. Lin, “Scalable differentiable physics for learning and control,” in International Conference on Machine Learning . PMLR, 2020, pp. 7847–7856
2020
Later among the works it cites.
Y. Hu, L. Anderson, T.-M. Li, Q. Sun, N. Carr, J. Ragan-Kelley, and F. Durand, “Difftaichi: Differentiable programming for physical simulation,” 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
S. Liu, T. Li, W. Chen, and H. Li, “Soft rasterizer: A differentiable renderer for image-based 3d reasoning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 7708–7717
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. Liang, M. Lin, and V. Koltun, “Differentiable cloth simulation for inverse problems,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
Y. Hu, J. Liu, A. Spielberg, J. B. Tenenbaum, W. T. Freeman, J. Wu, D. Rus, and W. Matusik, “Chainqueen: A real-time differentiable physical simulator for soft robotics,” in 2019 International conference on robotics and automation (ICRA) . IEEE, 2019, pp. 6265–6271
2019
Cited alongside, same era.
D. Hahn, P. Banzet, J. M. Bern, and S. Coros, “Real2sim: Visco-elastic parameter estimation from dynamic motion,” ACM Transactions on Graphics (TOG) , vol. 38, no. 6, pp. 1–13, 2019
2019
Cited alongside, same era.
P. Chang and T. Padif, “Sim2real2sim: Bridging the gap between simulation and real-world in flexible object manipulation,” in 2020 Fourth IEEE International Conference on Robotic Computing (IRC) . IEEE, 2020, pp. 56–62
2020
Cited alongside, same era.
L. Barcelos, R. Oliveira, R. Possas, L. Ott, and F. Ramos, “Disco: Double likelihood-free inference stochastic control,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 10 969–10 975
2020
Cited alongside, same era.
B. Mehta, M. Diaz, F. Golemo, C. J. Pal, and L. Paull, “Active domain randomization,” in Conference on Robot Learning . PMLR, 2020, pp. 1162–1176
2020
Cited alongside, same era.
M. Geilinger, D. Hahn, J. Zehnder, M. Bächer, B. Thomaszewski, and S. Coros, “Add: Analytically differentiable dynamics for multi-body systems with frictional contact,” ACM Transactions on Graphics (TOG) , vol. 39, no. 6, pp. 1–15, 2020
2020
Later among the works it cites.
S. Weiss, R. Maier, D. Cremers, R. Westermann, and N. Thuerey, “Correspondence-free material reconstruction using sparse surface constraints,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4686–4695
2020
Later among the works it cites.
A. Prakash, S. Debnath, J.-F. Lafleche, E. Cameracci, S. Birchfield, M. T. Law et al. , “Self-supervised real-to-sim scene generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 044–16 054
2021
Later among the works it cites.
F. Liu, Z. Li, Y. Han, J. Lu, F. Richter, and M. C. Yip, “Real-to-sim registration of deformable soft tissue with position-based dynamics for surgical robot autonomy,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 12 328–12 334
2021
Later among the works it cites.
S. Li, N. Figueroa, A. Shah, and J. A. Shah, “Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics,” in Robotics: Science and Systems (RSS) , 2021
2021
Later among the works it cites.
F. Muratore, T. G. Gruner, F. Wiese, B. Belousov, M. Gienger, and J. Peters, “Neural posterior domain randomization,” in Conference on Robot Learning . PMLR, 2021
2021
Later among the works it cites.
H. Yin, A. Varava, and D. Kragic, “Modeling, learning, perception, and control methods for deformable object manipulation,” Science Robotics , vol. 6, no. 54, 2021
2021
Later among the works it cites.
K. M. Jatavallabhula, M. Macklin, F. Golemo, V. Voleti, L. Petrini, M. Weiss, B. Considine, J. Parent-Levesque, K. Xie, K. Erleben et al. , “gradsim: Differentiable simulation for system identification and visuomotor control,” 2021
2021
Later among the works it cites.
T. Du, K. Wu, P. Ma, S. Wah, A. Spielberg, D. Rus, and W. Matusik, “DiffPD: Differentiable projective dynamics with contact,” arXiv e-prints , pp. arXiv–2101, 2021
2021
Later among the works it cites.
M. Lutter, J. Silberbauer, J. Watson, and J. Peters, “Differentiable physics models for real-world offline model-based reinforcement learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4163–4170
2021
Later among the works it cites.
M. A. Z. Mora, M. P. Peychev, S. Ha, M. Vechev, and S. Coros, “Pods: Policy optimization via differentiable simulation,” in International Conference on Machine Learning . PMLR, 2021, pp. 7805–7817
2021
Later among the works it cites.