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Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training.
Determining optical flow
Horn, B. K. and Schunck, B. G · 1981
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Three-dimensional scene flow
Vedula, S., Baker, S., Rander, P., Collins, R., and Kanade, T · 1999
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Online object tracking: A benchmark
Wu, Y., Lim, J., and Yang, M.-H · 2013
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Object scene flow for autonomous vehicles
Menze, M. and Geiger, A · 2015
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The” something something” video database for learning and evaluating visual common sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al · 2017
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Scaling egocentric vision: The epic-kitchens dataset
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., and Wray, M · 2018
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Rlbench: The robot learning benchmark & learning environment
James, S., Ma, Z., Arrojo, D. R., and Davison, A. J · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Ego4d: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., Martin, M., Nagarajan, T., Radosavovic, I., Ramakrishnan, S. K., Ryan, F., Sharma, J., Wray, M., Xu, M., Xu, E. Z., Zhao, C., Bansal, S., Batra, D., Cartillier, V., Crane, S., Do, T., Doulaty, M., Erapalli, A., Feichtenhofer, C., Fragomeni, A., Fu, Q., Fuegen, C., Gebreselasie, A., Gonzalez, C., Hillis, J., Huang, X., Huang, Y., Jia, W., Khoo, W., Kolar, J., Kottur, S., Kumar, A., Landini, F., Li, C., Li, Y., Li, Z., Mangalam, K., Modhugu, R., Munro, J., Murrell, T., Nishiyasu, T., Price, W., Puentes, P. R., Ramazanova, M., Sari, L., Somasundaram, K., Southerland, A., Sugano, Y., Tao, R., Vo, M., Wang, Y., Wu, X., Yagi, T., Zhu, Y., Arbelaez, P., Crandall, D., Damen, D., Farinella, G. M., Ghanem, B., Ithapu, V. K., Jawahar, C. V., Joo, H., Kitani, K., Li, H., Newcombe, R., Oliva, A., Park, H. S., Rehg, J. M., Sato, Y., Shi, J., Shou, M. Z., Torralba, A., Torresani, L., Yan, M., and Malik, J · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., and Komatsuzaki, A · 2021
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Raft-3d: Scene flow using rigid-motion embeddings
Teed, Z. and Deng, J · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., and Simonyan, K · 2022
Cited alongside, same era.
Pali: A jointly-scaled multilingual language-image model
Chen, X., Wang, X., Changpinyo, S., Piergiovanni, A., Padlewski, P., Salz, D., Goodman, S., Grycner, A., Mustafa, B., Beyer, L., et al · 2022
Cited alongside, same era.
IFOR: Iterative flow minimization for robotic object rearrangement, 2022
Goyal, A., Mousavian, A., Paxton, C., Chao, Y.-W., Okorn, B., Deng, J., and Fox, D · 2022
Cited alongside, same era.
Particle video revisited: Tracking through occlusions using point trajectories
Harley, A. W., Fang, Z., and Fragkiadaki, K · 2022
Cited alongside, same era.
RoboTAP: Tracking arbitrary points for few-shot visual imitation, 2023
Vecerik, M., Doersch, C., Yang, Y., Davchev, T., Aytar, Y., Zhou, G., Hadsell, R., Agapito, L., and Scholz, J · 2023
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Bharadhwaj, H., Mottaghi, R., Gupta, A., and Tulsiani, S · 2024
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al · 2024
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Rvt-2: Learning precise manipulation from few demonstrations
Goyal, A., Blukis, V., Xu, J., Guo, Y., Chao, Y.-W., and Fox, D · 2024
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Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
James, S., Wada, K., Laidlow, T., and Davison, A. J · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Seita, D., Wang, Y., Shetty, S. J., Li, E. Y., Erickson, Z., and Held, D · 2022
Cited alongside, same era.
Masked visual pre-training for motor control
Xiao, T., Radosavovic, I., Darrell, T., and Malik, J · 2022
Cited alongside, same era.
Open X-Embodiment: Robotic learning datasets and RT-X models
Collaboration, O. X.-E., O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., Tung, A., Bewley, A., Herzog, A., Irpan, A., Khazatsky, A., Rai, A., Gupta, A., Wang, A., Kolobov, A., Singh, A., Garg, A., Kembhavi, A., Xie, A., Brohan, A., Raffin, A., Sharma, A., Yavary, A., Jain, A., Balakrishna, A., Wahid, A., Burgess-Limerick, B., Kim, B., Schölkopf, B., Wulfe, B., Ichter, B., Lu, C., Xu, C., Le, C., Finn, C., Wang, C., Xu, C., Chi, C., Huang, C., Chan, C., Agia, C., Pan, C., Fu, C., Devin, C., Xu, D., Morton, D., Driess, D., Chen, D., Pathak, D., Shah, D., Büchler, D., Jayaraman, D., Kalashnikov, D., Sadigh, D., Johns, E., Foster, E., Liu, F., Ceola, F., Xia, F., Zhao, F., Frujeri, F. V., Stulp, F., Zhou, G., Sukhatme, G. S., Salhotra, G., Yan, G., Feng, G., Schiavi, G., Berseth, G., Kahn, G., Wang, G., Su, H., Fang, H.-S., Shi, H., Bao, H., Amor, H. B., Christensen, H. I., Furuta, H., Walke, H., Fang, H., Ha, H., Mordatch, I., Radosavovic, I., Leal, I., Liang, J., Abou-Chakra, J., Kim, J., Drake, J., Peters, J., Schneider, J., Hsu, J., Bohg, J., Bingham, J., Wu, J., Gao, J., Hu, J., Wu, J., Wu, J., Sun, J., Luo, J., Gu, J., Tan, J., Oh, J., Wu, J., Lu, J., Yang, J., Malik, J., Silvério, J., Hejna, J., Booher, J., Tompson, J., Yang, J., Salvador, J., Lim, J. J., Han, J., Wang, K., Rao, K., Pertsch, K., Hausman, K., Go, K., Gopalakrishnan, K., Goldberg, K., Byrne, K., Oslund, K., Kawaharazuka, K., Black, K., Lin, K., Zhang, K., Ehsani, K., Lekkala, K., Ellis, K., Rana, K., Srinivasan, K., Fang, K., Singh, K. P., Zeng, K.-H., Hatch, K., Hsu, K., Itti, L., Chen, L. Y., Pinto, L., Fei-Fei, L., Tan, L., Fan, L. J., Ott, L., Lee, L., Weihs, L., Chen, M., Lepert, M., Memmel, M., Tomizuka, M., Itkina, M., Castro, M. G., Spero, M., Du, M., Ahn, M., Yip, M. C., Zhang, M., Ding, M., Heo, M., Srirama, M. K., Sharma, M., Kim, M. J., Kanazawa, N., Hansen, N., Heess, N., Joshi, N. J., Suenderhauf, N., Liu, N., Palo, N. D., Shafiullah, N. M. M., Mees, O., Kroemer, O., Bastani, O., Sanketi, P. R., Miller, P. T., Yin, P., Wohlhart, P., Xu, P., Fagan, P. D., Mitrano, P., Sermanet, P., Abbeel, P., Sundaresan, P., Chen, Q., Vuong, Q., Rafailov, R., Tian, R., Doshi, R., Mart’in-Mart’in, R., Baijal, R., Scalise, R., Hendrix, R., Lin, R., Qian, R., Zhang, R., Mendonca, R., Shah, R., Hoque, R., Julian, R., Bustamante, S., Kirmani, S., Levine, S., Lin, S., Moore, S., Bahl, S., Dass, S., Sonawani, S., Song, S., Xu, S., Haldar, S., Karamcheti, S., Adebola, S., Guist, S., Nasiriany, S., Schaal, S., Welker, S., Tian, S., Ramamoorthy, S., Dasari, S., Belkhale, S., Park, S., Nair, S., Mirchandani, S., Osa, T., Gupta, T., Harada, T., Matsushima, T., Xiao, T., Kollar, T., Yu, T., Ding, T., Davchev, T., Zhao, T. Z., Armstrong, T., Darrell, T., Chung, T., Jain, V., Vanhoucke, V., Zhan, W., Zhou, W., Burgard, W., Chen, X., Chen, X., Wang, X., Zhu, X., Geng, X., Liu, X., Liangwei, X., Li, X., Pang, Y., Lu, Y., Ma, Y. J., Kim, Y., Chebotar, Y., Zhou, Y., Zhu, Y., Wu, Y., Xu, Y., Wang, Y., Bisk, Y., Cho, Y., Lee, Y., Cui, Y., Cao, Y., Wu, Y.-H., Tang, Y., Zhu, Y., Zhang, Y., Jiang, Y., Li, Y., Li, Y., Iwasawa, Y., Matsuo, Y., Ma, Z., Xu, Z., Cui, Z. J., Zhang, Z., Fu, Z., and Lin, Z · 2023
Cited alongside, same era.
Rvt: Robotic view transformer for 3d object manipulation
Goyal, A., Xu, J., Guo, Y., Blukis, V., Chao, Y.-W., and Fox, D · 2023
Cited alongside, same era.
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches, November 2023
Gu, J., Kirmani, S., Wohlhart, P., Lu, Y., Arenas, M. G., Rao, K., Yu, W., Fu, C., Gopalakrishnan, K., Xu, Z., Sundaresan, P., Xu, P., Su, H., Hausman, K., Finn, C., Vuong, Q., and Xiao, T · 2023
Cited alongside, same era.
Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollar, P., and Girshick, R · 2023
Cited alongside, same era.
Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., Vuong, Q., Kollar, T., Burchfiel, B., Tedrake, R., Sadigh, D., Levine, S., Liang, P., and Finn, C · 2024
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LLARVA: Vision-action instruction tuning enhances robot learning, 2024
Niu, D., Sharma, Y., Biamby, G., Quenum, J., Bai, Y., Shi, B., Darrell, T., and Herzig, R · 2024
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Octo: An open-source generalist robot policy
Team, O. M., Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., et al · 2024
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Any-point trajectory modeling for policy learning, 2024
Wen, C., Lin, X., So, J., Chen, K., Dou, Q., Gao, Y., and Abbeel, P · 2024
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SpatialTracker: Tracking any 2d pixels in 3d space, 2024
Xiao, Y., Wang, Q., Zhang, S., Xue, N., Peng, S., Shen, Y., and Zhou, X · 2024
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Flow as the cross-domain manipulation interface, 2024
Xu, M., Xu, Z., Xu, Y., Chi, C., Wetzstein, G., Veloso, M., and Song, S · 2024
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Latent action pretraining from videos, 2024
Ye, S., Jang, J., Jeon, B., Joo, S., Yang, J., Peng, B., Mandlekar, A., Tan, R., Chao, Y.-W., Lin, B. Y., Liden, L., Lee, K., Gao, J., Zettlemoyer, L., Fox, D., and Seo, M · 2024
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RoboPoint: A vision-language model for spatial affordance prediction for robotics, 2024
Yuan, W., Duan, J., Blukis, V., Pumacay, W., Krishna, R., Murali, A., Mousavian, A., and Fox, D · 2024
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Monst3r: A simple approach for estimating geometry in the presence of motion
Zhang, J., Herrmann, C., Hur, J., Jampani, V., Darrell, T., Cole, F., Sun, D., and Yang, M.-H · 2024
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Zheng, R., Liang, Y., Huang, S., Gao, J., III, H. D., Kolobov, A., Huang, F., and Yang, J · 2024
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Cotracker: It is better to track together
Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., and Rupprecht, C · 2025
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Manigaussian: Dynamic gaussian splatting for multi-task robotic manipulation
Lu, G., Zhang, S., Wang, Z., Liu, C., Lu, J., and Tang, Y · 2025
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Fast: Efficient action tokenization for vision-language-action models
Pertsch, K., Stachowicz, K., Ichter, B., Driess, D., Nair, S., Vuong, Q., Mees, O., Finn, C., and Levine, S · 2025
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