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In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision.
Stumbling corrective reaction: a phase-dependent compensatory reaction during locomotion
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2008
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Associations between the performance of scoring manoeuvres and lower-body strength and power in elite surfers
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Attention is all you need
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Neural message passing for quantum chemistry
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Proximal policy optimization algorithms
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine · 2017
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Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition
L. Dong, S. Xu, and B. Xu · 2018
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Nervenet: Learning structured policy with graph neural networks
T. Wang, R. Liao, J. Ba, and S. Fidler · 2018
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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 · 2020
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
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One policy to control them all: Shared modular policies for agent-agnostic control
W. Huang, I. Mordatch, and D. Pathak · 2020
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D4rl: Datasets for deep data-driven reinforcement learning, 2020
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine · 2020
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Decision transformer: Reinforcement learning via sequence modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
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Do transformers really perform badly for graph representation?
C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu · 2021
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My body is a cage: the role of morphology in graph-based incompatible control, 2021
Learning robust perceptive locomotion for quadrupedal robots in the wild
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022
T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré · 2022
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A hierarchical sensorimotor control framework for human-in-the-loop robotic hands
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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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General in-hand object rotation with vision and touch
H. Qi, B. Yi, S. Suresh, M. Lambeta, Y. Ma, R. Calandra, and J. Malik · 2023
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V. Kurin, M. Igl, T. Rocktäschel, W. Boehmer, and S. Whiteson · 2021
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Structure-aware transformer policy for inhomogeneous multi-task reinforcement learning
S. Hong, D. Yoon, and K.-E. Kim · 2021
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications, 2021
U. Alon and E. Yahav · 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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Online decision transformer
Q. Zheng, A. Zhang, and A. Grover · 2022
Cited alongside, same era.
Metamorph: Learning universal controllers with transformers
A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei · 2022
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Grpe: Relative positional encoding for graph transformer, 2022
W. Park, W. Chang, D. Lee, J. Kim, and S. won Hwang · 2022
Cited alongside, same era.
The power of the senses: Generalizable manipulation from vision and touch through masked multimodal learning, 2023
C. Sferrazza, Y. Seo, H. Liu, Y. Lee, and P. Abbeel · 2023
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Mocapact: A multi-task dataset for simulated humanoid control, 2023
N. Wagener, A. Kolobov, F. V. Frujeri, R. Loynd, C.-A. Cheng, and M. Hausknecht · 2023
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Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao · 2023
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Daydreamer: World models for physical robot learning
P. Wu, A. Escontrela, D. Hafner, P. Abbeel, and K. Goldberg · 2023
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Humanoid locomotion as next token prediction
I. Radosavovic, B. Zhang, B. Shi, J. Rajasegaran, S. Kamat, T. Darrell, K. Sreenath, and J. Malik · 2024
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Masked attention is all you need for graphs, 2024
D. Buterez, J. P. Janet, D. Oglic, and P. Lio · 2024
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Structural and positional ensembled encoding for graph transformer
J. Yeom, T. Kim, R. Chang, and K. Song · 2024
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Humanoidbench: Simulated humanoid benchmark for whole-body locomotion and manipulation
C. Sferrazza, D.-M. Huang, X. Lin, Y. Lee, and P. Abbeel · 2024
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