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The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that generalize in diverse scenarios.
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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A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Scaling egocentric vision: The epic-kitchens dataset
D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, et al · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, et al · 2018
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Parameter-efficient transfer learning for nlp
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Adapterfusion: Non-destructive task composition for transfer learning
J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych · 2020
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Siamese neural networks: An overview
D. Chicco · 2021
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Rrl: Resnet as representation for reinforcement learning
R. Shah and V. Kumar · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2021
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning, 2021
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State · 2021
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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
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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
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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Deep vit features as dense visual descriptors
S. Amir, Y. Gandelsman, S. Bagon, and T. Dekel · 2022
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One-shot transfer of affordance regions? affcorrs!
D. Hadjivelichkov, S. Zwane, L. Agapito, M. P. Deisenroth, and D. Kanoulas · 2022
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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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Vip: Towards universal visual reward and representation via value-implicit pre-training
Y. J. Ma, S. Sodhani, D. Jayaraman, O. Bastani, V. Kumar, and A. Zhang · 2023
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
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Real-world robot learning with masked visual pre-training
I. Radosavovic, T. Xiao, S. James, P. Abbeel, J. Malik, and T. Darrell · 2022
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R3m: A universal visual representation for robot manipulation
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta · 2022
Cited alongside, same era.
What makes representation learning from videos hard for control?
T. Zhao, S. Karamchetim, T. Kollar, C. Finn, and P. Liang · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2022
Cited alongside, same era.
The unsurprising effectiveness of pre-trained vision models for control
S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. Gupta · 2022
Cited alongside, same era.
Simple but effective: Clip embeddings for embodied ai
A. Khandelwal, L. Weihs, R. Mottaghi, and A. Kembhavi · 2022
Cited alongside, same era.
Language-driven representation learning for robotics
S. Karamcheti, S. Nair, A. S. Chen, T. Kollar, C. Finn, D. Sadigh, and P. Liang · 2023
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On pre-training for visuo-motor control: Revisiting a learning-from-scratch baseline
N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu, and X. Wang · 2023
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Vision transformer adapter for dense predictions
Z. Chen, Y. Duan, W. Wang, J. He, T. Lu, J. Dai, and Y. Qiao · 2023
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Genaug: Retargeting behaviors to unseen situations via generative augmentation
Z. Chen, S. Kiami, A. Gupta, and V. Kumar · 2023
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Scaling robot learning with semantically imagined experience
T. Yu, T. Xiao, A. Stone, J. Tompson, A. Brohan, S. Wang, J. Singh, C. Tan, J. Peralta, B. Ichter, et al · 2023
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Lossless adaptation of pretrained vision models for robotic manipulation
M. Sharma, C. Fantacci, Y. Zhou, S. Koppula, N. Heess, J. Scholz, and Y. Aytar · 2023
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End-to-end affordance learning for robotic manipulation
Y. Geng, B. An, H. Geng, Y. Chen, Y. Yang, and H. Dong · 2023
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