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Accurately modeling soft robots in simulation is computationally expensive and commonly falls short of representing the real world.
L. Ljung, System Identification: Theory for the User . Pearson Education, 1998
1998
Earlier work this paper cites.
M. Müller, B. Heidelberger, M. Hennix, and J. Ratcliff, “Position based dynamics,” Journal of Visual Communication and Image Representation , vol. 18, no. 2, pp. 109–118, 2007
2007
Earlier work this paper cites.
M. Skouras, B. Thomaszewski, S. Coros, B. Bickel, and M. Gross, “Computational design of actuated deformable characters,” ACM Transactions on Graphics (TOG) , vol. 32, no. 4, pp. 1–10, 2013
2013
Earlier work this paper cites.
S. Bouaziz, S. Martin, T. Liu, L. Kavan, and M. Pauly, “Projective dynamics,” ACM Transactions on Graphics , vol. 33, no. 4, pp. 1–11, 2014
2014
Earlier work this paper cites.
P. Battaglia, R. Pascanu, M. Lai, D. Jimenez Rezende et al. , “Interaction networks for learning about objects, relations and physics,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
R. K. Katzschmann, J. DelPreto, R. MacCurdy, and D. Rus, “Exploration of underwater life with an acoustically controlled soft robotic fish,” Science Robotics , vol. 3, no. 16, p. eaar3449, 2018
2018
Earlier work this paper cites.
A. Ajay, J. Wu, N. Fazeli, M. Bauza, L. P. Kaelbling, J. B. Tenenbaum, and A. Rodriguez, “Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 3066–3073
2018
Earlier work this paper cites.
M. Gazzola, L. Dudte, A. McCormick, and L. Mahadevan, “Forward and inverse problems in the mechanics of soft filaments,” Royal Society open science , vol. 5, no. 6, p. 171628, 2018. [Online]. Available: https://doi.org/10.1098/rsos.171628
2018
Earlier work this paper cites.
F. Golemo, A. A. Taiga, A. Courville, and P.-Y. Oudeyer, “Sim-to-real transfer with neural-augmented robot simulation,” in Proceedings of The 2nd Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Billard, A. Dragan, J. Peters, and J. Morimoto, Eds., vol. 87. PMLR, 29–31 Oct 2018, pp. 817–828. [Online]. Available: https://proceedings.mlr.press/v87/golemo18a.html
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Schneider, J. Dumas, X. Gao, M. Botsch, D. Panozzo, and D. Zorin, “Poly-spline finite-element method,” ACM Trans. Graph. , vol. 38, no. 3, Mar. 2019. [Online]. Available: http://doi.acm.org/10.1145/3313797
2019
Earlier work this paper cites.
C. F. Graetzel, A. Sheehy, and D. P. Noonan, “Robotic bronchoscopy drive mode of the auris monarch platform,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 3895–3901
2019
Earlier work this paper cites.
R. K. Katzschmann, M. Thieffry, O. Goury, A. Kruszewski, T.-M. Guerra, C. Duriez, and D. Rus, “Dynamically closed-loop controlled soft robotic arm using a reduced order finite element model with state observer,” in 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft) , 2019, pp. 717–724
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.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Cited alongside, same era.
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” IEEE Transactions on Robotics , vol. 36, no. 4, pp. 1307–1319, 2020
2021
Later among the works it cites.
E. Heiden, D. Millard, E. Coumans, Y. Sheng, and G. S. Sukhatme, “Neuralsim: Augmenting differentiable simulators with neural networks,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 9474–9481
2021
Later among the works it cites.
Y. Toshimitsu, K. W. Wong, T. Buchner, and R. Katzschmann, “SoPrA: Fabrication & dynamical modeling of a scalable soft continuum robotic arm with integrated proprioceptive sensing,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 9 2021. [Online]. Available: https://doi.org/10.1109%2Firos51168.2021.9636539
2021
Later among the works it cites.
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2020
Cited alongside, same era.
M. Li, Z. Ferguson, T. Schneider, T. Langlois, D. Zorin, D. Panozzo, C. Jiang, and D. M. Kaufman, “Incremental potential contact: Intersection- and inversion-free large deformation dynamics,” ACM Trans. Graph. (SIGGRAPH) , vol. 39, no. 4, 2020
2020
Cited alongside, same era.
A. Allevato, E. S. Short, M. Pryor, and A. Thomaz, “Tunenet: One-shot residual tuning for system identification and sim-to-real robot task transfer,” in Conference on Robot Learning . PMLR, 2020, pp. 445–455
2020
Cited alongside, same era.
K. Chin, T. Hellebrekers, and C. Majidi, “Machine learning for soft robotic sensing and control,” Advanced Intelligent Systems , vol. 2, no. 6, p. 1900171, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Macklin and M. Muller, “A Constraint-based Formulation of Stable Neo-Hookean Materials,” in Motion, Interaction and Games . Virtual Event Switzerland: ACM, Nov. 2021, pp. 1–7. [Online]. Available: https://dl.acm.org/doi/10.1145/3487983.3488289
2021
Cited alongside, same era.
T. Du, K. Wu, P. Ma, S. Wah, A. Spielberg, D. Rus, and W. Matusik, “Diffpd: Differentiable projective dynamics,” ACM Trans. Graph. , vol. 41, no. 2, nov 2021. [Online]. Available: https://doi.org/10.1145/3490168
2021
Cited alongside, same era.
T. Du, J. Hughes, S. Wah, W. Matusik, and D. Rus, “Underwater soft robot modeling and control with differentiable simulation,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4994–5001, 2021
2021
Cited alongside, same era.
J. Z. Zhang, Y. Zhang, P. Ma, E. Nava, T. Du, P. Arm, W. Matusik, and R. K. Katzschmann, “Sim2real for soft robotic fish via differentiable simulation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 12 598–12 605
2022
Later among the works it cites.
M. Dubied, M. Y. Michelis, A. Spielberg, and R. K. Katzschmann, “Sim-to-real for soft robots using differentiable fem: Recipes for meshing, damping, and actuation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5015–5022, 2022
2022
Later among the works it cites.
B. G. Cangan, S. E. Navarro, B. Yang, Y. Zhang, C. Duriez, and R. K. Katzschmann, “Model-based disturbance estimation for a fiber-reinforced soft manipulator using orientation sensing,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 9424–9430
2022
Later among the works it cites.
2022
Later among the works it cites.
K. Junge, C. Pires, and J. Hughes, “Lab2field transfer of a robotic raspberry harvester enabled by a soft sensorized physical twin,” Communications Engineering , vol. 2, no. 1, p. 40, 2023
2023
Later among the works it cites.
E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature , vol. 620, no. 7976, pp. 982–987, 2023
2023
Later among the works it cites.
C. Laschi, T. G. Thuruthel, F. Lida, R. Merzouki, and E. Falotico, “Learning-based control strategies for soft robots: Theory, achievements, and future challenges,” IEEE Control Systems Magazine , vol. 43, no. 3, pp. 100–113, 2023
2023
Later among the works it cites.