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Reliable robotic grasping, especially with deformable objects such as fruits, remains a challenging task due to underactuated contact interactions with a gripper, unknown object dynamics and geometries.
S. Hochreiter, Y. Bengio, et al. , “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” in A Field Guide to Dynamical Recurrent Neural Networks . IEEE Press, 2001
2001
Earlier work this paper cites.
N. Wettels, A. R. Parnandi, J.-H. Moon, G. E. Loeb, and G. S. Sukhatme, “Grip control using biomimetic tactile sensing systems,” IEEE/ASME Transactions On Mechatronics , vol. 14, no. 6, pp. 718–723, 2009
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Wu, I. Yildirim, J. J. Lim, B. Freeman, and J. Tenenbaum, “Galileo: Perceiving physical object properties by integrating a physics engine with deep learning,” in Advances in Neural Information Processing Systems , vol. 28, 2015
2015
Earlier work this paper cites.
J. Hughes, U. Culha, F. Giardina, F. Guenther, A. Rosendo, and F. Iida, “Soft manipulators and grippers: A review,” Frontiers in Robotics and AI , vol. 3, p. 69, 2016
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.
W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors , vol. 17, no. 12, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Yuan, S. Wang, S. Dong, and E. Adelson, “Connecting look and feel: Associating the visual and tactile properties of physical materials,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5580–5588
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, et al. , “Attention is all you need,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Li, S. Dong, and E. Adelson, “Slip detection with combined tactile and visual information,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 7772–7777
2018
Earlier work this paper cites.
R. Calandra, A. Owens, D. Jayaraman, et al. , “More than a feeling: Learning to grasp and regrasp using vision and touch,” IEEE Robotics and Automation Letters , vol. 3, pp. 3300–3307, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Z. Xu, J. Wu, A. Zeng, J. B. Tenenbaum, and S. Song, “Densephysnet: Learning dense physical object representations via multi-step dynamic interactions,” in Robotics: Science and Systems (RSS) , 2019
2019
Cited alongside, same era.
S. Dong, D. Ma, E. Donlon, and A. Rodriguez, “Maintaining grasps within slipping bounds by monitoring incipient slip,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019
2019
Cited alongside, same era.
J. Lin, R. Calandra, and S. Levine, “Learning to identify object instances by touch: Tactile recognition via multimodal matching,” in International Conference on Robotics and Automation , 2019, pp. 3644–3650
2019
Cited alongside, same era.
N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 9917–9924
2020
Cited alongside, same era.
G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?” in ICML , vol. 2, no. 3, 2021, p. 4
2021
Closest in time.
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, “Vivit: A video vision transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6836–6846
2021
Closest in time.
Y. She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” The International Journal of Robotics Research , vol. 40, no. 12-14, pp. 1385–1401, 2021
2021
Closest in time.
2021
Closest in time.
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C. Wang, S. Wang, B. Romero, F. Veiga, and E. Adelson, “Swingbot: Learning physical features from in-hand tactile exploration for dynamic swing-up manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 5633–5640
2020
Cited alongside, same era.
W. Friedl and M. A. Roa, “Clash—a compliant sensorized hand for handling delicate objects,” Frontiers in Robotics and AI , vol. 6, p. 138, 2020
2020
Cited alongside, same era.
D. Kim, J. Lee, W.-Y. Chung, and J. Lee, “Artificial intelligence-based optimal grasping control,” Sensors , vol. 20, no. 21, 2020
2020
Cited alongside, same era.
Y. Zhang, W. Yuan, Z. Kan, and M. Y. Wang, “Towards learning to detect and predict contact events on vision-based tactile sensors,” in Conference on Robot Learning , 2020, pp. 1395–1404
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Zuo, H. Jiang, Z. Li, T. Zhao, and H. Zha, “Transformer hawkes process,” in International conference on machine learning . PMLR, 2020, pp. 11 692–11 702
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Wang, Y. She, B. Romero, and E. Adelson, “Gelsight wedge: Measuring high-resolution 3d contact geometry with a compact robot finger,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 6468–6475
2021
Cited alongside, same era.
2021
Closest in time.
L. Pozzi, M. Gandolla, F. Pura, M. Maccarini, A. Pedrocchi, F. Braghin, D. Piga, and L. Roveda, “Grasping learning, optimization, and knowledge transfer in the robotics field,” Scientific Reports , vol. 12, no. 1, pp. 1–11, 2022
2022
Closest in time.
J. Jiang, G. Cao, A. Butterworth, T.-T. Do, and S. Luo, “Where shall i touch? vision-guided tactile poking for transparent object grasping,” IEEE/ASME Transactions on Mechatronics , 2022
2022
Closest in time.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,” in Computer Vision–ECCV: European Conference, Tel Aviv, Israel, Proceedings . Springer, 2022, pp. 107–124
2022
Closest in time.
2022
Closest in time.
R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang, “Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers,” in International Conference on Learning Representations , 2022
2022
Closest in time.
L. Wang, X. Meng, Y. Xiang, and D. Fox, “Hierarchical policies for cluttered-scene grasping with latent plans,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2883–2890, 2022
2022
Closest in time.
M. Monastirsky, O. Azulay, and A. Sintov, “Learning to throw with a handful of samples using decision transformers,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 576–583, 2023
2023
Closest in time.