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Increasingly large imitation learning datasets are being collected with the goal of training foundation models for robotics.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Dart: Noise injection for robust imitation learning
M. Laskey, J. Lee, R. Fox, A. Dragan, and K. Goldberg · 2017
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Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2017
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Multiple interactions made easy (mime): Large scale demonstrations data for imitation
P. Sharma, L. Mohan, L. Pinto, and A. Gupta · 2018
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Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al · 2018
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
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Unsupervised cross-lingual representation learning at scale
A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov · 2019
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S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang · 2019
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Robonet: Large-scale multi-robot learning
S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, S. Levine, and C. Finn · 2019
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Semantic redundancies in image-classification datasets: The 10% you don’t need
V. Birodkar, H. Mobahi, and S. Bengio · 2019
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Accelerating deep learning by focusing on the biggest losers
A. H. Jiang, D. L.-K. Wong, G. Zhou, D. G. Andersen, J. Dean, G. R. Ganger, G. Joshi, M. Kaminksy, M. Kozuch, Z. C. Lipton, et al · 2019
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Distributionally robust language modeling
Y. Oren, S. Sagawa, T. B. Hashimoto, and P. Liang · 2019
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Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, and M. Jordan · 2019
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The Pile: An 800gb dataset of diverse text for language modeling
L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, S. Presser, and C. Leahy · 2020
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Coresets for data-efficient training of machine learning models
B. Mirzasoleiman, J. Bilmes, and J. Leskovec · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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What matters in learning from offline human demonstrations for robot manipulation
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
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Visual imitation made easy
S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto · 2021
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Training data subset search with ensemble active learning
K. Chitta, J. M. Álvarez, E. Haussmann, and C. Farabet · 2021
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Deep learning on a data diet: Finding important examples early in training
M. Paul, S. Ganguli, and G. K. Dziugaite · 2021
Cited alongside, same era.
Bridge data: Boosting generalization of robotic skills with cross-domain datasets
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine · 2021
Cited alongside, same era.
The surprising effectiveness of representation learning for visual imitation, 2021
J. Pari, N. M. Shafiullah, S. P. Arunachalam, and L. Pinto · 2021
Cited alongside, same era.
Rt-1: Robotics transformer for real-world control at scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K.-H. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. Ryoo, G. Salazar, P. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich · 2022
Cited alongside, same era.
Decomposing the generalization gap in imitation learning for visual robotic manipulation
A. Xie, L. Lee, T. Xiao, and C. Finn · 2023
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Behavior retrieval: Few-shot imitation learning by querying unlabeled datasets
M. Du, S. Nair, D. Sadigh, and C. Finn · 2023
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Dinov2: Learning robust visual features without supervision
M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al · 2023
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Stable video diffusion: Scaling latent video diffusion models to large datasets
A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. English, V. Voleti, A. Letts, et al · 2023
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Latent plans for task agnostic offline reinforcement learning
E. Rosete-Beas, O. Mees, G. Kalweit, J. Boedecker, and W. Burgard · 2022
Cited alongside, same era.
Viola: Imitation learning for vision-based manipulation with object proposal priors
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu · 2022
Cited alongside, same era.
Learning and retrieval from prior data for skill-based imitation learning
S. Nasiriany, T. Gao, A. Mandlekar, and Y. Zhu · 2022
Cited alongside, same era.
Ego4d: Around the world in 3,000 hours of egocentric video
K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Hamburger, H. Jiang, M. Liu, X. Liu, et al · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al · 2022
Cited alongside, same era.
Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning
E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn · 2022
Cited alongside, same era.
Video pretraining (vpt): Learning to act by watching unlabeled online videos
B. Baker, I. Akkaya, P. Zhokov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune · 2022
Cited alongside, same era.
Redpajama: an open dataset for training large language models, October 2023
T. Computer · 2023
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D4: Improving llm pretraining via document de-duplication and diversification
K. Tirumala, D. Simig, A. Aghajanyan, and A. Morcos · 2023
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Semdedup: Data-efficient learning at web-scale through semantic deduplication, 2023
A. Abbas, K. Tirumala, D. Simig, S. Ganguli, and A. S. Morcos · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn · 2023
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Idql: Implicit q-learning as an actor-critic method with diffusion policies, 2023
P. Hansen-Estruch, I. Kostrikov, M. Janner, J. G. Kuba, and S. Levine · 2023
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Multi-stage cable routing through hierarchical imitation learning
J. Luo, C. Xu, X. Geng, G. Feng, K. Fang, L. Tan, S. Schaal, and S. Levine · 2023
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Clvr jaco play dataset, 2023
S. Dass, J. Yapeter, J. Zhang, J. Zhang, K. Pertsch, S. Nikolaidis, and J. J. Lim · 2023
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Train offline, test online: A real robot learning benchmark
G. Zhou, V. Dean, M. K. Srirama, A. Rajeswaran, J. Pari, K. Hatch, A. Jain, T. Yu, P. Abbeel, L. Pinto, et al · 2023
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Droid: A large-scale in-the-wild robot manipulation dataset
A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y. Chen, K. Ellis, et al · 2024
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Fineweb, April 2024
G. Penedo, H. Kydlíček, L. von Werra, and T. Wolf · 2024
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A survey on data selection for language models, 2024
A. Albalak, Y. Elazar, S. M. Xie, S. Longpre, N. Lambert, X. Wang, N. Muennighoff, B. Hou, L. Pan, H. Jeong, C. Raffel, S. Chang, T. Hashimoto, and W. Y. Wang · 2024
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Efficient data collection for robotic manipulation via compositional generalization, 2024
J. Gao, A. Xie, T. Xiao, C. Finn, and D. Sadigh · 2024
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Data quality in imitation learning
S. Belkhale, Y. Cui, and D. Sadigh · 2024
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Octo: An open-source generalist robot policy
Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y. Tan, L. Y. Chen, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine · 2024
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Openvla: An open-source vision-language-action model
M. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, Q. Vuong, T. Kollar, B. Burchfiel, R. Tedrake, D. Sadigh, S. Levine, P. Liang, and C. Finn · 2024
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Robocasa: Large-scale simulation of everyday tasks for generalist robots
S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu · 2024
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Demystifying CLIP data
H. Xu, S. Xie, X. Tan, P.-Y. Huang, R. Howes, V. Sharma, S.-W. Li, G. Ghosh, L. Zettlemoyer, and C. Feichtenhofer · 2024
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Automatic data curation for self-supervised learning: A clustering-based approach
H. V. Vo, V. Khalidov, T. Darcet, T. Moutakanni, N. Smetanin, M. Szafraniec, H. Touvron, C. Couprie, M. Oquab, A. Joulin, et al · 2024
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Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
L. Soldaini, R. Kinney, A. Bhagia, D. Schwenk, D. Atkinson, R. Authur, B. Bogin, K. Chandu, J. Dumas, Y. Elazar, V. Hofmann, A. H. Jha, S. Kumar, L. Lucy, X. Lyu, N. Lambert, I. Magnusson, J. Morrison, N. Muennighoff, A. Naik, C. Nam, M. E. Peters, A. Ravichander, K. Richardson, Z. Shen, E. Strubell, N. Subramani, O. Tafjord, P. Walsh, L. Zettlemoyer, N. A. Smith, H. Hajishirzi, I. Beltagy, D. Groeneveld, J. Dodge, and K. Lo · 2024
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Evaluating real-world robot manipulation policies in simulation
X. Li, K. Hsu, J. Gu, K. Pertsch, O. Mees, H. R. Walke, C. Fu, I. Lunawat, I. Sieh, S. Kirmani, S. Levine, J. Wu, C. Finn, H. Su, Q. Vuong, and T. Xiao · 2024
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Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Z. Fu, T. Z. Zhao, and C. Finn · 2024
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