Fetching the paper…
Reading the bibliography…
As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal performances.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Deep spatial autoencoders for visuomotor learning
Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P · 2016
Earlier work this paper cites.
Multi-view 3d models from single images with a convolutional network
Tatarchenko, M., Dosovitskiy, A., and Brox, T · 2016
Earlier work this paper cites.
Interpretable transformations with encoder-decoder networks
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
Earlier work this paper cites.
Learning actionable representations from visual observations
Dwibedi, D., Tompson, J., Lynch, C., and Sermanet, P · 2018
Earlier work this paper cites.
Neural scene representation and rendering
Eslami, S. A., Jimenez Rezende, D., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al · 2018
Earlier work this paper cites.
Soft actor-critic algorithms and applications
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al · 2018
Earlier work this paper cites.
Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., and Brain, G · 2018
Earlier work this paper cites.
In-place scene labelling and understanding with implicit scene representation
Zhi, S., Laidlow, T., Leutenegger, S., and Davison, A. J · 2018
Earlier work this paper cites.
Dream to control: Learning behaviors by latent imagination
Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 2019
Earlier work this paper cites.
Unsupervised learning of object keypoints for perception and control
Kulkarni, T. D., Gupta, A., Ionescu, C., Borgeaud, S., Reynolds, M., Zisserman, A., and Mnih, V · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Earlier work this paper cites.
Self-supervised policy adaptation during deployment
Hansen, N., Jangir, R., Sun, Y., Alenyà, G., Abbeel, P., Efros, A. A., Pinto, L., and Wang, X · 2020
Earlier work this paper cites.
Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning
Manuelli, L., Li, Y., Florence, P., and Tedrake, R · 2020
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J., Ramamoorthi, R., and Ng, R · 2020
Cited alongside, same era.
Data-efficient reinforcement learning with self-predictive representations
Schwarzer, M., Anand, A., Goel, R., Hjelm, R. D., Courville, A., and Bachman, P · 2020
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Yarats, D., Kostrikov, I., and Fergus, R · 2020
Cited alongside, same era.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2020
pixelnerf: Neural radiance fields from one or few images
Yu, A., Ye, V., Tancik, M., and Kanazawa, A · 2021
Later among the works it cites.
Reinforcement learning with neural radiance fields
Driess, D., Schubert, I., Florence, P., Li, Y., and Toussaint, M · 2022
Later among the works it cites.
Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation
Fu, X., Zhang, S., Chen, T., Lu, Y., Zhu, L., Zhou, X., Geiger, A., and Liao, Y · 2022
Later among the works it cites.
Discrete factorial representations as an abstraction for goal conditioned reinforcement learning
Islam, R., Zang, H., Goyal, A., Lamb, A., Kawaguchi, K., Li, X., Laroche, R., Bengio, Y., and Combes, R. T. D · 2022
Later among the works it cites.
Multi-view dreaming: Multi-view world model with contrastive learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
Cited alongside, same era.
Exploring simple siamese representation learning
Chen, X. and He, K · 2021
Cited alongside, same era.
Behavior from the void: Unsupervised active pre-training
Liu, H. and Abbeel, P · 2021
Cited alongside, same era.
Nerf in the wild: Neural radiance fields for unconstrained photo collections
Martin-Brualla, R., Radwan, N., Sajjadi, M. S., Barron, J. T., Dosovitskiy, A., and Duckworth, D · 2021
Cited alongside, same era.
Decoupling representation learning from reinforcement learning
Stooke, A., Lee, K., Abbeel, P., and Laskin, M · 2021
Cited alongside, same era.
Ibrnet: Learning multi-view image-based rendering
Wang, Q., Wang, Z., Genova, K., Srinivasan, P. P., Zhou, H., Barron, J. T., Martin-Brualla, R., Snavely, N., and Funkhouser, T · 2021
Cited alongside, same era.
Representation matters: offline pretraining for sequential decision making
Yang, M. and Nachum, O · 2021
Cited alongside, same era.
Kinose, A., Okada, M., Okumura, R., and Taniguchi, T · 2022
Later among the works it cites.
Decomposing nerf for editing via feature field distillation
Kobayashi, S., Matsumoto, E., and Sitzmann, V · 2022
Later among the works it cites.
Panoptic neural fields: A semantic object-aware neural scene representation
Kundu, A., Genova, K., Yin, X., Fathi, A., Pantofaru, C., Guibas, L. J., Tagliasacchi, A., Dellaert, F., and Funkhouser, T · 2022
Later among the works it cites.
3d neural scene representations for visuomotor control
Li, Y., Li, S., Sitzmann, V., Agrawal, P., and Torralba, A · 2022
Later among the works it cites.
Vrl3: A data-driven framework for visual deep reinforcement learning
Wang, C., Luo, X., Ross, K., and Li, D · 2022
Later among the works it cites.
Task-induced representation learning
Yamada, J., Pertsch, K., Gunjal, A., and Lim, J. J · 2022
Later among the works it cites.
Integrating contrastive learning with dynamic models for reinforcement learning from images
You, B., Arenz, O., Chen, Y., and Peters, J · 2022
Later among the works it cites.
Learning visual robotic control efficiently with contrastive pre-training and data augmentation
Zhan, A., Zhao, R., Pinto, L., Abbeel, P., and Laskin, M · 2022
Later among the works it cites.