Fetching the paper…
Reading the bibliography…
Contact force in contact-rich environments is an essential modality for robots to perform general-purpose manipulation tasks, as it provides information to compensate for the deficiencies of visual and proprioceptive data in collision perception, high-precision grasping, and efficient manipulation.
N. Hogan, “Impedance control: An approach to manipulation.” in 1984 American Control Conference , 1984
1984
Earlier work this paper cites.
H. Liu, L. Wang, and F. Wang, “Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment,” Frontiers of Mechanical Engineering in China , vol. 2, pp. 168–174, 2007
2007
Earlier work this paper cites.
B. Siciliano, “Springer handbook of robotics,” Springer-Verlag google schola , vol. 2, pp. 15–35, 2008
2008
Earlier work this paper cites.
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel, “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” in 2018 IEEE international conference on robotics and automation (ICRA) , pp. 5628–5635. IEEE, 2018
2018
Earlier work this paper cites.
T. Ren, Y. Dong, D. Wu, and K. Chen, “Learning-based variable compliance control for robotic assembly,” Journal of Mechanisms and Robotics , vol. 10, no. 6, p. 061008, 2018
2018
Earlier work this paper cites.
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 5745–5753, 2019
2019
Earlier work this paper cites.
H. Li, L. Wu, H. Wang, C. Han, W. Quan, and J. Zhao, “Hand gesture recognition enhancement based on spatial fuzzy matching in leap motion,” IEEE Transactions on Industrial Informatics , vol. 16, DOI 10.1109/TII.2019.2931140 , no. 3, pp. 1885–1894, 2020
2020
Earlier work this paper cites.
S. Li, J. Jiang, P. Ruppel, H. Liang, X. Ma, N. Hendrich, F. Sun, and J. Zhang, “A mobile robot hand-arm teleoperation system by vision and imu,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , DOI 10.1109/IROS45743.2020.9340738 , pp. 10 900–10 906, 2020
2020
Earlier work this paper cites.
A. Handa, K. Van Wyk, W. Yang, J. Liang, Y.-W. Chao, Q. Wan, S. Birchfield, N. Ratliff, and D. Fox, “Dexpilot: Vision-based teleoperation of dexterous robotic hand-arm system,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , pp. 9164–9170. IEEE, 2020
2020
Earlier work this paper cites.
C. C. Beltran-Hernandez, D. Petit, I. G. Ramirez-Alpizar, and K. Harada, “Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach,” Applied Sciences , vol. 10, no. 19, p. 6923, 2020
2020
Earlier work this paper cites.
C. C. Beltran-Hernandez, D. Petit, I. G. Ramirez-Alpizar, T. Nishi, S. Kikuchi, T. Matsubara, and K. Harada, “Learning force control for contact-rich manipulation tasks with rigid position-controlled robots,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5709–5716, 2020
2020
Cited alongside, same era.
T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” Advances in neural information processing systems , vol. 35, pp. 26 565–26 577, 2022
2022
Cited alongside, same era.
Y. Qin, W. Yang, B. Huang, K. Van Wyk, H. Su, X. Wang, Y.-W. Chao, and D. Fox, “Anyteleop: A general vision-based dexterous robot arm-hand teleoperation system,” in Robotics: Science and Systems , 2023
2023
Cited alongside, same era.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Robotics: Science and Systems , 2023
2023
Cited alongside, same era.
W. Wang, C. Zeng, Z. Lu, and C. Yang, “A novel robust imitation learning framework for dual-arm object-moving tasks,” IEEE Transactions on Industrial Electronics , vol. 71, DOI 10.1109/TIE.2024.3387098 , no. 12, pp. 16 068–16 076, 2024
2024
Closest in time.
D. Wang, J. Lin, L. Ma, D. Huang, and Y. Wu, “Image-based visual-admittance control with prescribed performance of manipulators in feature space,” IEEE Transactions on Industrial Electronics , DOI 10.1109/TIE.2024.3468609 , pp. 1–11, 2024
2024
Closest in time.
A. Prasad, K. Lin, J. Wu, L. Zhou, and J. Bohg, “Consistency policy: Accelerated visuomotor policies via consistency distillation,” in Robotics: Science and Systems , 2024
2024
Closest in time.
Z. Fu, Q. Zhao, Q. Wu, G. Wetzstein, and C. Finn, “Humanplus: Humanoid shadowing and imitation from humans,” in Conference on Robot Learning (CoRL) , 2024
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Darvish, L. Penco, J. Ramos, R. Cisneros, J. Pratt, E. Yoshida, S. Ivaldi, and D. Pucci, “Teleoperation of humanoid robots: A survey,” IEEE Transactions on Robotics , vol. 39, no. 3, pp. 1706–1727, 2023
2023
Cited alongside, same era.
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” in Robotics: Science and Systems , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. Wang, L. Fan, J. Sun, R. Zhang, L. Fei-Fei, D. Xu, Y. Zhu, and A. Anandkumar, “Mimicplay: Long-horizon imitation learning by watching human play,” in Conference on Robot Learning (CoRL) , 2023
2023
Cited alongside, same era.
T. Yang, Y. Jing, H. Wu, J. Xu, K. Sima, G. Chen, Q. Sima, and T. Kong, “Moma-force: Visual-force imitation for real-world mobile manipulation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 6847–6852. IEEE, 2023
2023
Cited alongside, same era.
K. Shaw, A. Agarwal, and D. Pathak, “Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning,” in Robotics: Science and Systems , 2023
2023
Cited alongside, same era.
C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in Robotics: Science and Systems , 2024
2024
Closest in time.
C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu, “Dexcap: Scalable and portable mocap data collection system for dexterous manipulation,” in Robotics: Science and Systems , 2024
2024
Closest in time.
A. Sridhar, D. Shah, C. Glossop, and S. Levine, “Nomad: Goal masked diffusion policies for navigation and exploration,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , pp. 63–70. IEEE, 2024
2024
Closest in time.
S. Haldar, Z. Peng, and L. Pinto, “Baku: An efficient transformer for multi-task policy learning,” Advances in Neural Information Processing Systems , 2024
2024
Closest in time.
T. Kamijo, C. C. Beltran-Hernandez, and M. Hamaya, “Learning variable compliance control from a few demonstrations for bimanual robot with haptic feedback teleoperation system,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024
2024
Closest in time.