Fetching the paper…
Reading the bibliography…
Performing language-conditioned robotic manipulation tasks in unstructured environments is highly demanded for general intelligent robots.
Tenorth, M., Nyga, D., Beetz, M.: Understanding and executing instructions for everyday manipulation tasks from the world wide web. In: ICRA. pp. 1486–1491. IEEE (2010)
2010
Earlier work this paper cites.
Tellex, S., Kollar, T., Dickerson, S., Walter, M., Banerjee, A., Teller, S., Roy, N.: Understanding natural language commands for robotic navigation and mobile manipulation. In: AAAI (2011)
2011
Earlier work this paper cites.
Ha, D., Schmidhuber, J.: Recurrent world models facilitate policy evolution. NeurIPS 31
2018
Earlier work this paper cites.
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al.: Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation. arXiv (2018)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., Hsieh, C.J.: Large batch optimization for deep learning: Training bert in 76 minutes. arXiv (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
James, S., Ma, Z., Arrojo, D.R., Davison, A.J.: Rlbench: The robot learning benchmark & learning environment. RA-L (2020)
2020
Earlier work this paper cites.
Laskin, M., Srinivas, A., Abbeel, P.: Curl: Contrastive unsupervised representations for reinforcement learning. In: ICML (2020)
2020
Earlier work this paper cites.
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al.: Mastering atari, go, chess and shogi by planning with a learned model. Nature 588
2020
Earlier work this paper cites.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)
2021
Earlier work this paper cites.
Hansen, N., Wang, X.: Generalization in reinforcement learning by soft data augmentation. In: ICRA (2021)
2021
Earlier work this paper cites.
Jaegle, A., Gimeno, F., Brock, A., Vinyals, O., Zisserman, A., Carreira, J.: Perceiver: General perception with iterative attention. In: ICML (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. CACM 65
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
Earlier work this paper cites.
Ye, W., Liu, S., Kurutach, T., Abbeel, P., Gao, Y.: Mastering atari games with limited data. NeurIPS 34
2021
Earlier work this paper cites.
Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Sindhwani, V., et al.: Transporter networks: Rearranging the visual world for robotic manipulation. In: CoRL (2021)
2021
Earlier work this paper cites.
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al.: Rt-1: Robotics transformer for real-world control at scale. arXiv (2022)
2022
Earlier work this paper cites.
Driess, D., Schubert, I., Florence, P., Li, Y., Toussaint, M.: Reinforcement learning with neural radiance fields. NeurIPS (2022)
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Hafner, D., Lee, K.H., Fischer, I., Abbeel, P.: Deep hierarchical planning from pixels. NeurIPS 35
2022
Earlier work this paper cites.
Hu, A., Corrado, G., Griffiths, N., Murez, Z., Gurau, C., Yeo, H., Kendall, A., Cipolla, R., Shotton, J.: Model-based imitation learning for urban driving. NeurIPS 35
2022
Earlier work this paper cites.
James, S., Wada, K., Laidlow, T., Davison, A.J.: Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation. In: CVPR (2022)
2022
Earlier work this paper cites.
Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., Finn, C.: Bc-z: Zero-shot task generalization with robotic imitation learning. In: CoRL (2022)
2022
Earlier work this paper cites.
Li, Y., Li, S., Sitzmann, V., Agrawal, P., Torralba, A.: 3d neural scene representations for visuomotor control. In: CoRL. pp. 112–123 (2022)
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Nair, S., Rajeswaran, A., Kumar, V., Finn, C., Gupta, A.: R3m: A universal visual representation for robot manipulation. arXiv (2022)
2022
Cited alongside, same era.
Parisi, S., Rajeswaran, A., Purushwalkam, S., Gupta, A.: The unsurprising effectiveness of pre-trained vision models for control. In: ICML (2022)
2022
Cited alongside, same era.
Qian, G., Li, Y., Peng, H., Mai, J., Hammoud, H., Elhoseiny, M., Ghanem, B.: Pointnext: Revisiting pointnet++ with improved training and scaling strategies. NeurIPS 35
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
Cited alongside, same era.
Seo, Y., Lee, K., James, S.L., Abbeel, P.: Reinforcement learning with action-free pre-training from videos. In: ICML. pp. 19561–19579 (2022)
2022
2023
Later among the works it cites.
Shim, D., Lee, S., Kim, H.J.: Snerl: Semantic-aware neural radiance fields for reinforcement learning. ICML (2023)
2023
Later among the works it cites.
Shridhar, M., Manuelli, L., Fox, D.: Perceiver-actor: A multi-task transformer for robotic manipulation. In: CoRL (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Shridhar, M., Manuelli, L., Fox, D.: Cliport: What and where pathways for robotic manipulation. In: CoRL (2022)
2022
Cited alongside, same era.
Abou-Chakra, J., Rana, K., Dayoub, F., Sünderhauf, N.: Physically embodied gaussian splatting: Embedding physical priors into a visual 3d world model for robotics. In: CoRL (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Gervet, T., Xian, Z., Gkanatsios, N., Fragkiadaki, K.: Act3d: 3d feature field transformers for multi-task robotic manipulation. In: CoRL. pp. 3949–3965 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, P., Escontrela, A., Hafner, D., Abbeel, P., Goldberg, K.: Daydreamer: World models for physical robot learning. In: CoRL. pp. 2226–2240 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Ze, Y., Hansen, N., Chen, Y., Jain, M., Wang, X.: Visual reinforcement learning with self-supervised 3d representations. RA-L (2023)
2023
Later among the works it cites.
Ze, Y., Yan, G., Wu, Y.H., Macaluso, A., Ge, Y., Ye, J., Hansen, N., Li, L.E., Wang, X.: Gnfactor: Multi-task real robot learning with generalizable neural feature fields. In: CoRL. pp. 284–301. PMLR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Chen, G., Wang, W.: A survey on 3d gaussian splatting. arXiv preprint arXiv:2401.03890 (2024)
2024
Closest in time.
2024
Closest in time.
Du, Y., Yang, S., Dai, B., Dai, H., Nachum, O., Tenenbaum, J., Schuurmans, D., Abbeel, P.: Learning universal policies via text-guided video generation. NeurIPS 36
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Wu, J., Ma, H., Deng, C., Long, M.: Pre-training contextualized world models with in-the-wild videos for reinforcement learning. NeurIPS 36
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Zhou, S., Chang, H., Jiang, S., Fan, Z., Zhu, Z., Xu, D., Chari, P., You, S., Wang, Z., Kadambi, A.: Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields. In: CVPR. pp. 21676–21685 (2024)
2024
Closest in time.
2024
Closest in time.