Fetching the paper…
Reading the bibliography…
Visual pre-training with large-scale real-world data has made great progress in recent years, showing great potential in robot learning with pixel observations.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2009
2009
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems , 2015
2015
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015
2015
Earlier work this paper cites.
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015
2015
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The Journal of Machine Learning Research , vol. 17, pp. 1334–1373, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke et al. , “Scalable deep reinforcement learning for vision-based robotic manipulation,” in Conference on Robot Learning , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,” Conference on Robot Learning (CoRL) , 2019
2019
Earlier work this paper cites.
M. Shridhar, D. Mittal, and D. Hsu, “Ingress: Interactive visual grounding of referring expressions,” The International Journal of Robotics Research , vol. 39, pp. 217–232, 2020
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020
2020
Cited alongside, same era.
Y.-C. Chen, L. Li, L. Yu, A. El Kholy, F. Ahmed, Z. Gan, Y. Cheng, and J. Liu, “Uniter: Universal image-text representation learning,” in European Conference on Computer Vision , 2020
2020
Cited alongside, same era.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on Robot Learning , 2020
2020
Cited alongside, same era.
A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion, “Mdetr-modulated detection for end-to-end multi-modal understanding,” in Proceedings of the IEEE International Conference on Computer Vision , 2021
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning , 2021
2021
Later among the works it cites.
2022
Later among the works it cites.
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3m: A universal visual representation for robot manipulation,” in 6th Annual Conference on Robot Learning , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,” in Conference on Robot Learning , 2020
2020
Cited alongside, same era.
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters , vol. 5, pp. 3019–3026, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International Conference on Machine Learning , 2020
2020
Cited alongside, same era.
X. Chen, S. Xie, and K. He, “An empirical study of training self-supervised vision transformers,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2021
2021
Cited alongside, same era.
H. Zhang, Y. Lu, C. Yu, D. Hsu, X. Lan, and N. Zheng, “INVIGORATE: Interactive Visual Grounding and Grasping in Clutter,” in Robotics: Science and Systems , 2021
2021
Cited alongside, same era.
J. Li, R. Selvaraju, A. Gotmare, S. Joty, C. Xiong, and S. C. H. Hoi, “Align before fuse: Vision and language representation learning with momentum distillation,” in Advances in Neural Information Processing Systems , 2021
2021
Cited alongside, same era.
I. Radosavovic, T. Xiao, S. James, P. Abbeel, J. Malik, and T. Darrell, “Real-world robot learning with masked visual pre-training,” in Conference on Robot Learning
Cited in the paper.
K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Hamburger, H. Jiang, M. Liu, X. Liu et al. , “Ego4d: Around the world in 3,000 hours of egocentric video,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2022
2022
Later among the works it cites.
Z. Tong, Y. Song, J. Wang, and L. Wang, “Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training,” Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters , 2022
2022
Later among the works it cites.