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It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Deep auto-encoder neural networks in reinforcement learning
S. Lange and M. Riedmiller · 2010
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
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Deep spatial autoencoders for visuomotor learning
C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 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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Pves: Position-velocity encoders for unsupervised learning of structured state representations
R. Jonschkowski, R. Hafner, J. Scholz, and M. Riedmiller · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Learning actionable representations from visual observations
D. Dwibedi, J. Tompson, C. Lynch, and P. Sermanet · 2018
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Neural scene representation and rendering
S. A. Eslami, D. Jimenez Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, et al · 2018
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain · 2018
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State representation learning for control: An overview
T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, and D. Filliat · 2018
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Representation learning with contrastive predictive coding
A. Van den Oord, Y. Li, and O. Vinyals · 2018
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Unsupervised learning of object keypoints for perception and control
T. D. Kulkarni, A. Gupta, C. Ionescu, S. Borgeaud, M. Reynolds, A. Zisserman, and V. Mnih · 2019
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kpam: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. Florence, and R. Tedrake · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
V. Sitzmann, M. Zollhöfer, and G. Wetzstein · 2019
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Improving sample efficiency in model-free reinforcement learning from images
D. Yarats, A. Zhang, I. Kostrikov, B. Amos, J. Pineau, and R. Fergus · 2019
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
S. Saito, Z. Huang, R. Natsume, S. Morishima, A. Kanazawa, and H. Li · 2019
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A. Raffin, A. Hill, M. Ernestus, A. Gleave, A. Kanervisto, and N. Dormann · 2019
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Curl: Contrastive unsupervised representations for reinforcement learning
M. Laskin, A. Srinivas, and P. Abbeel · 2020
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Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning
L. Manuelli, Y. Li, P. Florence, and R. Tedrake · 2020
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S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency
M. Vecerik, J.-B. Regli, O. Sushkov, D. Barker, R. Pevceviciute, T. Rothörl, C. Schuster, R. Hadsell, L. Agapito, and J. Scholz · 2020
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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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Learning precise 3d manipulation from multiple uncalibrated cameras
I. Akinola, J. Varley, and D. Kalashnikov · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
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NeRD: Neural reflectance decomposition from image collections
M. Boss, R. Braun, V. Jampani, J. T. Barron, C. Liu, and H. Lensch · 2020
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NeRV: Neural reflectance and visibility fields for relighting and view synthesis
P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron · 2020
Cited alongside, same era.
Neural sparse voxel fields
L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt · 2020
Cited alongside, same era.
pixelnerf: Neural radiance fields from one or few images
A. Yu, V. Ye, M. Tancik, and A. Kanazawa · 2021
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Ibrnet: Learning multi-view image-based rendering
Q. Wang, Z. Wang, K. Genova, P. P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser · 2021
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Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering
W. Yuan, Z. Lv, T. Schmidt, and S. Lovegrove · 2021
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Unsupervised discovery of object radiance fields
H.-X. Yu, L. J. Guibas, and J. Wu · 2021
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Learning object-compositional neural radiance field for editable scene rendering
B. Yang, Y. Zhang, Y. Xu, Y. Li, H. Zhou, H. Bao, G. Zhang, and Z. Cui · 2021
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D. Lindell, J. Martel, and G. Wetzstein · 2020
Cited alongside, same era.
DeRF: Decomposed radiance fields
D. Rebain, W. Jiang, S. Yazdani, K. Li, K. M. Yi, and A. Tagliasacchi · 2020
Cited alongside, same era.
NERF++: Analyzing and improving neural radiance fields
K. Zhang, G. Riegler, N. Snavely, and V. Koltun · 2020
Cited alongside, same era.
GIRAFFE: Representing scenes as compositional generative neural feature fields
M. Niemeyer and A. Geiger · 2020
Cited alongside, same era.
Object-centric neural scene rendering
M. Guo, A. Fathi, J. Wu, and T. Funkhouser · 2020
Cited alongside, same era.
Learning compositional radiance fields of dynamic human heads
Z. Wang, T. Bagautdinov, S. Lombardi, T. Simon, J. Saragih, J. Hodgins, and M. Zollhöfer · 2020
Cited alongside, same era.
Neural scene graphs for dynamic scenes
J. Ost, F. Mannan, N. Thuerey, J. Knodt, and F. Heide · 2020
Cited alongside, same era.
H. Zhang, R. Wang, J. Zhang, C. Li, G. Yang, P. Spincemaille, T. Nguyen, and Y. Wang · 2021
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iNeRF: Inverting neural radiance fields for pose estimation
L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin · 2021
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Dex-nerf: Using a neural radiance field to grasp transparent objects
J. Ichnowski, Y. Avigal, J. Kerr, and K. Goldberg · 2021
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Learning neural implicit functions as object representations for robotic manipulation
J.-S. Ha, D. Driess, and M. Toussaint · 2021
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Neural descriptor fields: Se (3)-equivariant object representations for manipulation
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann · 2021
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Multi-modal mutual information (mummi) training for robust self-supervised deep reinforcement learning
K. Chen, Y. Lee, and H. Soh · 2021
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Decoupling representation learning from reinforcement learning
A. Stooke, K. Lee, P. Abbeel, and M. Laskin · 2021
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Giraffe: Representing scenes as compositional generative neural feature fields
M. Niemeyer and A. Geiger · 2021
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Decomposing 3d scenes into objects via unsupervised volume segmentation
K. Stelzner, K. Kersting, and A. R. Kosiorek · 2021
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Learning models as functionals of signed-distance fields for manipulation planning
D. Driess, J.-S. Ha, M. Toussaint, and R. Tedrake · 2021
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3d neural scene representations for visuomotor control
Y. Li, S. Li, V. Sitzmann, P. Agrawal, and A. Torralba · 2022
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Block-nerf: Scalable large scene neural view synthesis
M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
T. Müller, A. Evans, C. Schied, and A. Keller · 2022
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Learning multi-object dynamics with compositional neural radiance fields
D. Driess, Z. Huang, Y. Li, R. Tedrake, and M. Toussaint · 2022
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Vision-only robot navigation in a neural radiance world
M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager · 2022
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NeRF-Supervision: Learning dense object descriptors from neural radiance fields
L. Yen-Chen, P. Florence, J. T. Barron, T.-Y. Lin, A. Rodriguez, and P. Isola · 2022
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Virdo: Visio-tactile implicit representations of deformable objects
Y. Wi, P. Florence, A. Zeng, and N. Fazeli · 2022
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Integrating contrastive learning with dynamic models for reinforcement learning from images
B. You, O. Arenz, Y. Chen, and J. Peters · 2022
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Multi-view dreaming: Multi-view world model with contrastive learning
A. Kinose, M. Okada, R. Okumura, and T. Taniguchi · 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
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The unsurprising effectiveness of pre-trained vision models for control
S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. Gupta · 2022
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Xirl: Cross-embodiment inverse reinforcement learning
K. Zakka, A. Zeng, P. Florence, J. Tompson, J. Bohg, and D. Dwibedi · 2022
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Masked visual pre-training for motor control
T. Xiao, I. Radosavovic, T. Darrell, and J. Malik · 2022
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Reinforcement learning with action-free pre-training from videos
Y. Seo, K. Lee, S. James, and P. Abbeel · 2022
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