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Classical policy search algorithms for robotics typically require performing extensive explorations, which are time-consuming and expensive to implement with real physical platforms.
D. Navarro-Alarcon, H. M. Yip, Z. Wang, Y.-H. Liu, F. Zhong, T. Zhang, and P. Li, “Automatic 3-d manipulation of soft objects by robotic arms with an adaptive deformation model,” IEEE Transactions on Robotics
2016
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A. Hajiloo, M. Keshmiri, W.-F. Xie, and T.-T. Wang, “Robust online model predictive control for a constrained image-based visual servoing,” IEEE Transactions on Industrial Electronics
2016
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T. Zhang, Z. McCarthy, O. Jow, D. Lee, K. Goldberg, and P. Abbeel, “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” 2018 IEEE International Conference on Robotics and Automation (ICRA)
2017
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Y. Duan, M. Andrychowicz, B. Stadie, O. Jonathan Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba, “One-shot imitation learning,” Advances in neural information processing systems
2017
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning
2017
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P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine, “Time-contrastive networks: Self-supervised learning from video,” 2018 IEEE International Conference on Robotics and Automation (ICRA)
2017
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K. Chatzilygeroudis, V. Vassiliades, F. Stulp, S. Calinon, and J.-B. Mouret, “A survey on policy search algorithms for learning robot controllers in a handful of trials,” IEEE Transactions on Robotics
2018
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J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Conference on Robot Learning
2018
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A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al
2018
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F. Torabi, G. Warnell, and P. Stone, “Behavioral cloning from observation,” ArXiv
2018
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D. Navarro-Alarcon and Y.-H. Liu, “Fourier-based shape servoing: A new feedback method to actively deform soft objects into desired 2-d image contours,” IEEE Trans. on Robotics
2018
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H. Wang, B. Yang, J. Wang, X. Liang, W. Chen, and Y. hui Liu, “Adaptive visual servoing of contour features,” IEEE/ASME Transactions on Mechatronics
2018
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D. Seita, N. Jamali, M. Laskey, A. K. Tanwani, R. Berenstein, P. Baskaran, S. Iba, J. F. Canny, and K. Goldberg, “Deep transfer learning of pick points on fabric for robot bed-making,” in International Symposium of Robotics Research
2018
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D. Pathak, P. Mahmoudieh, G. Luo, P. Agrawal, D. Chen, Y. Shentu, E. Shelhamer, J. Malik, A. A. Efros, and T. Darrell, “Zero-shot visual imitation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2018
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H. Zhang, P. Lai, S. Paul, S. Kothawade, S. Nikolaidis, H. Zhang, P. Lai, S. Paul, S. Kothawade, and S. Nikolaidis, “Learning collaborative action plans from youtube videos,” in International Symposium of Robotics Research
2019
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O. Mees, M. Merklinger, G. Kalweit, and W. Burgard, “Adversarial skill networks: Unsupervised robot skill learning from video,” 2020 IEEE International Conference on Robotics and Automation (ICRA)
2019
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F. Liu, Z. Ling, T. Mu, and H. Su, “State alignment-based imitation learning,” ArXiv
2019
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H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3d object reconstruction from a single image,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2019
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F. Liu, Z. Ling, T. Mu, and H. Su, “State alignment-based imitation learning,” in International Conference on Learning Representations
2020
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B. S. Pavse, F. Torabi, J. Hanna, G. Warnell, and P. Stone, “Ridm: Reinforced inverse dynamics modeling for learning from a single observed demonstration,” IEEE Robotics and Automation Letters
2020
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D. McConachie, A. Dobson, M. Ruan, and D. Berenson, “Manipulating deformable objects by interleaving prediction, planning, and control,” The International Journal of Robotics Research
R. Antonova, P. Shi, H. Yin, Z. Weng, and D. Kragic, “Dynamic environments with deformable objects,” in NeurIPS Datasets and Benchmarks
2021
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W. Yan, A. Vangipuram, P. Abbeel, and L. Pinto, “Learning predictive representations for deformable objects using contrastive estimation,” in Conference on Robot Learning
2021
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X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, “Deep reinforcement learning: A survey,” IEEE Transactions on Neural Networks and Learning Systems
2022
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D. Zhang, W. Fan, J. Lloyd, C. Yang, and N. F. Lepora, “One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring,” IEEE Transactions on Automation Science and Engineering
2022
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2020
Cited alongside, same era.
G. Wang, M. Xin, W. Wu, Z. Liu, and H. Wang, “Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers.,” IEEE transactions on neural networks and learning systems
2020
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2020
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J. Qian, T. Weng, L. Zhang, B. Okorn, and D. Held, “Cloth region segmentation for robust grasp selection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2020
Cited alongside, same era.
P. Mitrano, D. Mcconachie, and D. Berenson, “Learning where to trust unreliable models in an unstructured world for deformable object manipulation,” Science Robotics
2021
Cited alongside, same era.
K. Zakka, A. Zeng, P. R. Florence, J. Tompson, J. Bohg, and D. Dwibedi, “Xirl: Cross-embodiment inverse reinforcement learning,” in Conference on Robot Learning
2021
Cited alongside, same era.
K. Zorina, J. Carpentier, J. Sivic, and V. Petr’ik, “Learning to manipulate tools by aligning simulation to video demonstration,” IEEE Robotics Autom. Lett
2021
Cited alongside, same era.
E. Johns, “Coarse-to-fine imitation learning: Robot manipulation from a single demonstration,” 2021 IEEE International Conference on Robotics and Automation (ICRA)
2021
Cited alongside, same era.
X. Sun, J. Li, A. V. Kovalenko, W. Feng, and Y. Ou, “Integrating reinforcement learning and learning from demonstrations to learn nonprehensile manipulation,” IEEE Transactions on Automation Science and Engineering
2022
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X. Liu, P. Huang, and Z. Liu, “A novel contact state estimation method for robot manipulation skill learning via environment dynamics and constraints modeling,” IEEE Transactions on Automation Science and Engineering
2022
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E. Valassakis, G. Papagiannis, N. Di Palo, and E. Johns, “Demonstrate once, imitate immediately (dome): Learning visual servoing for one-shot imitation learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2022
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S. Bahl, A. Gupta, and D. Pathak, “Human-to-robot imitation in the wild,” ArXiv
2022
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X. Deng, J. Liu, H. Gong, H. Gong, and J. Huang, “A human-robot collaboration method using a pose estimation network for robot learning of assembly manipulation trajectories from demonstration videos,” IEEE Transactions on Industrial Informatics
2022
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J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. LI, J. Pan, W. Yuan, and M. Gienger, “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robotics & Automation Magazine
2022
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F. Zhang and Y. Demiris, “Learning garment manipulation policies toward robot-assisted dressing,” Science Robotics
2022
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C. Wang, Y. Zhang, X. Zhang, Z. Wu, X. Zhu, S. Jin, T. Tang, and M. Tomizuka, “Offline-online learning of deformation model for cable manipulation with graph neural networks,” IEEE Robotics and Automation Letters
2022
Later among the works it cites.
S. Huo, A. Duan, C. Li, P. Zhou, W. Ma, H. Wang, and D. Navarro-Alarcon, “Keypoint-based planar bimanual shaping of deformable linear objects under environmental constraints with hierarchical action framework,” IEEE Robotics and Automation Letters
2022
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G. Salhotra, I. Liu, M. Dominguez-Kuhne, and G. S. Sukhatme, “Learning deformable object manipulation from expert demonstrations,” IEEE Robotics and Automation Letters
2022
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M. Lippi, P. Poklukar, M. C. Welle, A. Varava, H. Yin, A. Marino, and D. Kragic, “Enabling visual action planning for object manipulation through latent space roadmap,” IEEE Transactions on Robotics
2023
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