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Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems.
2016
Earlier work this paper cites.
S. Luo, J. Bimbo, R. Dahiya, and H. Liu, “Robotic tactile perception of object properties: A review,” Mechatronics , vol. 48, pp. 54–67, 2017
2017
Earlier work this paper cites.
S. Krishnan, A. Garg, S. Patil, C. Lea, G. Hager, P. Abbeel, and K. Goldberg, “Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning,” The International journal of robotics research , vol. 36, no. 13-14, pp. 1595–1618, 2017
2017
Earlier work this paper cites.
T. P. Tomo, M. Regoli, A. Schmitz, L. Natale, H. Kristanto, S. Somlor, L. Jamone, G. Metta, and S. Sugano, “A new silicone structure for uskin—a soft, distributed, digital 3-axis skin sensor and its integration on the humanoid robot icub,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 2584–2591, 2018
2018
Earlier work this paper cites.
A. Billard and D. Kragic, “Trends and challenges in robot manipulation,” Science , vol. 364, no. 6446, p. eaat8414, 2019
2019
Earlier work this paper cites.
Q. Li, O. Kroemer, Z. Su, F. F. Veiga, M. Kaboli, and H. J. Ritter, “A review of tactile information: Perception and action through touch,” IEEE Transactions on Robotics , vol. 36, no. 6, pp. 1619–1634, 2020
2020
Earlier work this paper cites.
H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, “Recent advances in robot learning from demonstration,” Annual review of control, robotics, and autonomous systems , vol. 3, pp. 297–330, 2020
2020
Earlier work this paper cites.
F. Xie, A. Chowdhury, M. De Paolis Kaluza, L. Zhao, L. Wong, and R. Yu, “Deep imitation learning for bimanual robotic manipulation,” Advances in neural information processing systems , vol. 33, pp. 2327–2337, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
I. Radosavovic, X. Wang, L. Pinto, and J. Malik, “State-only imitation learning for dexterous manipulation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 7865–7871
2021
Earlier work this paper cites.
D. Zhang, Q. Li, Y. Zheng, L. Wei, D. Zhang, and Z. Zhang, “Explainable hierarchical imitation learning for robotic drink pouring,” IEEE Transactions on Automation Science and Engineering , vol. 19, no. 4, pp. 3871–3887, 2021
2021
Earlier work this paper cites.
D. Meli and P. Fiorini, “Unsupervised identification of surgical robotic actions from small non-homogeneous datasets,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 8205–8212, 2021
2021
Earlier work this paper cites.
C. Yu and P. Wang, “Dexterous manipulation for multi-fingered robotic hands with reinforcement learning: a review,” Frontiers in Neurorobotics , vol. 16, p. 861825, 2022
2022
Earlier work this paper cites.
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) . IEEE, 2022, pp. 8614–8621
2022
Earlier work this paper cites.
C. Coppola and L. Jamone, “Master of puppets: Multi-modal robot activity segmentation from teleoperated demonstrations,” in 2022 IEEE International Conference on Development and Learning (ICDL) . IEEE, 2022, pp. 88–94
2022
Cited alongside, same era.
Y. Zhu, P. Stone, and Y. Zhu, “Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4126–4133, 2022
2022
Cited alongside, same era.
K. Yao and A. Billard, “Exploiting kinematic redundancy for robotic grasping of multiple objects,” IEEE Transactions on Robotics , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Í. Elguea-Aguinaco, A. Serrano-Muñoz, D. Chrysostomou, I. Inziarte-Hidalgo, S. Bøgh, and N. Arana-Arexolaleiba, “A review on reinforcement learning for contact-rich robotic manipulation tasks,” Robotics and Computer-Integrated Manufacturing , vol. 81, p. 102517, 2023
2023
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2023
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2023
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S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto, “Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation,” in 2023 ieee international conference on robotics and automation (icra) . IEEE, 2023, pp. 5954–5961
2023
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F. Khadivar and A. Billard, “Adaptive fingers coordination for robust grasp and in-hand manipulation under disturbances and unknown dynamics,” IEEE Transactions on Robotics , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam et al. , “Dextreme: Transfer of agile in-hand manipulation from simulation to reality,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5977–5984
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Jiang, G. Cao, J. Deng, T.-T. Do, and S. Luo, “Robotic perception of transparent objects: A review,” IEEE Transactions on Artificial Intelligence , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
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2023
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2023
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2023
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2023
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E. Triantafyllidis, F. Acero, Z. Liu, and Z. Li, “Hybrid hierarchical learning for solving complex sequential tasks using the robotic manipulation network roman,” Nature Machine Intelligence , vol. 5, no. 9, pp. 991–1005, 2023
2023
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2023
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2024
Closest in time.
J. Luo, C. Xu, X. Geng, G. Feng, K. Fang, L. Tan, S. Schaal, and S. Levine, “Multi-stage cable routing through hierarchical imitation learning,” IEEE Transactions on Robotics , 2024
2024
Closest in time.