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We demonstrate model-based, visual robot manipulation of linear deformable objects.
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2015
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M. Jaderberg, K. Simonyan, A. Zisserman, et al. , “Spatial transformer networks,” International Conference on Neural Information Processing Systems (NIPS) , pp. 2017–2025, Dec. 2015
2015
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Y. Li, Y. Wang, Y. Yue, D. Xu, M. Case, S.-F. Chang, E. Grinspun, and P. K. Allen, “Model-driven feedforward prediction for manipulation of deformable objects,” IEEE Transactions on Automation Science and Engineering , vol. 15, pp. 1621–1638, 2016
2016
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T. Tang, Y. Fan, H. Lin, and M. Tomizuka, “State estimation for deformable objects by point registration and dynamic simulation,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 2427–2433, Sep. 2017
2017
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F. Ebert, C. Finn, A. X. Lee, and S. Levine, “Self-supervised visual planning with temporal skip connections,” Conference on Robot Learning , pp. 344–356, 2017
2017
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A. Clegg, “Learning to dress: synthesizing human dressing motion via deep reinforcement learning,” ACM Trans. Graph. , vol. 37, pp. 179:1–179:10, 2018
2018
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F. Ebert, C. Finn, S. Dasari, A. Xie, A. X. Lee, and S. Levine, “Visual foresight: Model-based deep reinforcement learning for vision-based robotic control,” arXiv: 1812.00568 , 2018
2018
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T. Tang, C. Liu, W. Chen, and M. Tomizuka, “Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 2689–2696, Sep. 2016
2016
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P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” International Conference on Neural Information Processing Systems (NIPS) , Dec. 2016
2016
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G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” IEEE International Conference on Robotics and Automation (ICRA) , pp. 1433–1440, May 2016
2016
Cited alongside, same era.
A. Nair, D. Chen, P. Agrawal, P. Isola, P. Abbeel, J. Malik, and S. Levine, “Combining self-supervised learning and imitation for vision-based rope manipulation,” IEEE International Conference on Robotics and Automation (ICRA) , pp. 2146–2153, May 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Wu, E. Lu, P. Kohli, B. Freeman, and J. Tenenbaum, “Learning to see physics via visual de-animation,” International Conference on Neural Information Processing Systems (NIPS) , pp. 153–164, Dec. 2017
2017
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Physbam: physically based animation. http://physbam.stanford.edu/
Cited in the paper.
A. Pumarola, A. Agudo, L. Porzi, A. Sanfeliu, V. Lepetit, and F. Moreno-Noguer, “Geometry-aware network for non-rigid shape prediction from a single view,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
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2018
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A. Byravan, F. Leeb, F. Meier, and D. Fox, “SE3-pose-nets: Structured deep dynamics models for visuomotor control,” IEEE International Conference on Robotics and Automation (ICRA) , pp. 1–8, May 2018
2018
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F. Ebert, S. Dasari, A. X. Lee, S. Levine, and C. Finn, “Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning,” Conference on Robot Learning , vol. 87, pp. 983–993, 2018
2018
Later among the works it cites.
Y. Li, J. Wu, J.-Y. Zhu, J. B. Tenenbaum, A. Torralba, and R. Tedrake, “Propagation networks for model-based control under partial observation,” IEEE International Conference on Robotics and Automation (ICRA) , May 2019
2019
Closest in time.
A. Wang, T. Kurutach, K. Liu, P. Abbeel, and A. Tamar, “Learning robotic manipulation through visual planning and acting,” Proceedings of Robotics: Science and Systems , 2019
2019
Closest in time.