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Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies.
The influence curve and its role in robust estimation
F. R. Hampel · 1974
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Tiago: the modular robot that adapts to different research needs
J. Pages, L. Marchionni, and F. Ferro · 2016
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 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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Garage: A toolkit for reproducible reinforcement learning research
T. garage contributors · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
V. Feldman and C. Zhang · 2020
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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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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
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Datamodels: Predicting predictions from training data
A. Ilyas, S. M. Park, L. Engstrom, G. Leclerc, and A. Madry · 2022
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Gmflow: Learning optical flow via global matching
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, and D. Tao · 2022
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Measuring the effect of training data on deep learning predictions via randomized experiments
J. Lin, A. Zhang, M. Lécuyer, J. Li, A. Panda, and S. Sen · 2022
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Learning and retrieval from prior data for skill-based imitation learning
S. Nasiriany, T. Gao, A. Mandlekar, and Y. Zhu · 2022
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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, et al · 2022
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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
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al · 2023
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Bridgedata v2: A dataset for robot learning at scale
H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen-Estruch, A. W. He, V. Myers, M. J. Kim, M. Du, et al · 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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Libero: Benchmarking knowledge transfer for lifelong robot learning
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone · 2023
Cited alongside, same era.
Mimicgen: A data generation system for scalable robot learning using human demonstrations
A. Mandlekar, S. Nasiriany, B. Wen, I. Akinola, Y. Narang, L. Fan, Y. Zhu, and D. Fox · 2023
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Doremi: Optimizing data mixtures speeds up language model pretraining
S. M. Xie, H. Pham, X. Dong, N. Du, H. Liu, Y. Lu, P. S. Liang, Q. V. Le, T. Ma, and A. W. Yu · 2023
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TRAK: Attributing model behavior at scale
S. M. Park, K. Georgiev, A. Ilyas, G. Leclerc, and A. Madry · 2023
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A data-based perspective on transfer learning
S. Jain, H. Salman, A. Khaddaj, E. Wong, S. M. Park, and A. Madry · 2023
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Dsdm: Model-aware dataset selection with datamodels
L. Engstrom, A. Feldmann, and A. Madry · 2024
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Less: selecting influential data for targeted instruction tuning
M. Xia, S. Malladi, S. Gururangan, S. Arora, and D. Chen · 2024
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Tsds: Data selection for task-specific model finetuning
Z. Liu, A. Karbasi, and T. Rekatsinas · 2024
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Improving subgroup robustness via data selection
S. Jain, K. Hamidieh, K. Georgiev, A. Ilyas, M. Ghassemi, and A. Madry · 2024
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Training data attribution via approximate unrolled differentiation
J. Bae, W. Lin, J. Lorraine, and R. Grosse · 2024
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Generalized group data attribution
D. Ley, S. Srinivas, S. Zhang, G. Rusak, and H. Lakkaraju · 2024
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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
Cited alongside, same era.
π 0 \pi_{0} : A vision-language-action flow model for general robot control
K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter, et al · 2024
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Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain, et al · 2024
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Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot
H.-S. Fang, H. Fang, Z. Tang, J. Liu, C. Wang, J. Wang, H. Zhu, and C. Lu · 2024
Cited alongside, same era.
Openvla: An open-source vision-language-action model
M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, et al · 2024
Cited alongside, same era.
Distilling and retrieving generalizable knowledge for robot manipulation via language corrections
L. Zha, Y. Cui, L.-H. Lin, M. Kwon, M. G. Arenas, A. Zeng, F. Xia, and D. Sadigh · 2024
Cited alongside, same era.
Strap: Robot sub-trajectory retrieval for augmented policy learning
M. Memmel, J. Berg, B. Chen, A. Gupta, and J. Francis · 2024
Cited alongside, same era.
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Telemoma: A modular and versatile teleoperation system for mobile manipulation
S. Dass, W. Ai, Y. Jiang, S. Singh, J. Hu, R. Zhang, P. Stone, B. Abbatematteo, and R. Martín-Martín · 2024
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Gr00t n1: An open foundation model for generalist humanoid robots
J. Bjorck, F. Castañeda, N. Cherniadev, X. Da, R. Ding, L. Fan, Y. Fang, D. Fox, F. Hu, S. Huang, et al · 2025
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Fine-tuning vision-language-action models: Optimizing speed and success
M. J. Kim, C. Finn, and P. Liang · 2025
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Optimizing ml training with metagradient descent
L. Engstrom, A. Ilyas, B. Chen, A. Feldmann, W. Moses, and A. Madry · 2025
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Robot data curation with mutual information estimators
J. Hejna, S. Mirchandani, A. Balakrishna, A. Xie, A. Wahid, J. Tompson, P. Sanketi, D. Shah, C. Devin, and D. Sadigh · 2025
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Curating demonstrations using online experience
A. S. Chen, A. M. Lessing, Y. Liu, and C. Finn · 2025
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Collage: Adaptive fusion-based retrieval for augmented policy learning
S. Kumar, S. Dass, G. Pavlakos, and R. Martín-Martín · 2025
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Cupid: Curating data your robot loves with influence functions
C. Agia, R. Sinha, J. Yang, R. Antonova, M. Pavone, H. Nishimura, M. Itkina, and J. Bohg · 2025
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Magic: Near-optimal data attribution for deep learning, 2025
A. Ilyas and L. Engstrom · 2025
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Datarater: Meta-learned dataset curation
D. A. Calian, G. Farquhar, I. Kemaev, L. M. Zintgraf, M. Hessel, J. Shar, J. Oh, A. György, T. Schaul, J. Dean, et al · 2025
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Sim-and-real co-training: A simple recipe for vision-based robotic manipulation
A. Maddukuri, Z. Jiang, L. Y. Chen, S. Nasiriany, Y. Xie, Y. Fang, W. Huang, Z. Wang, Z. Xu, N. Chernyadev, et al · 2025
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Small-to-large generalization: Training data influences models consistently across scale
A. Khaddaj, L. Engstrom, and A. Madry · 2025
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What matters in learning from large-scale datasets for robot manipulation
V. Saxena, M. Bronars, N. R. Arachchige, K. Wang, W. C. Shin, S. Nasiriany, A. Mandlekar, and D. Xu · 2025
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