| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4927 | 2021 |
| R3m: A universal visual representation for robot manipulation S Nair, A Rajeswaran, V Kumar, C Finn, A Gupta arXiv preprint arXiv:2203.12601, 2022 | 550 | 2022 |
| RoboNet: Large-scale multi-robot learning S Dasari, F Ebert, S Tian, S Nair, B Bucher, K Schmeckpeper, S Singh, ... Conference on Robot Learning (CoRL), 2019 | 323 | 2019 |
| Recovery rl: Safe reinforcement learning with learned recovery zones B Thananjeyan, A Balakrishna, S Nair, M Luo, K Srinivasan, M Hwang, ... IEEE Robotics and Automation Letters 6 (3), 4915-4922, 2021 | 266 | 2021 |
| Openvla: An open-source vision-language-action model MJ Kim, K Pertsch, S Karamcheti, T Xiao, A Balakrishna, S Nair, ... arXiv preprint arXiv:2406.09246, 2024 | 255 | 2024 |
| Neural task programming: Learning to generalize across hierarchical tasks D Xu, S Nair, Y Zhu, J Gao, A Garg, L Fei-Fei, S Savarese 2018 IEEE international conference on robotics and automation (ICRA), 3795-3802, 2018 | 248 | 2018 |
| Neural task graphs: Generalizing to unseen tasks from a single video demonstration DA Huang, S Nair, D Xu, Y Zhu, A Garg, L Fei-Fei, S Savarese, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 167 | 2019 |
| Learning language-conditioned robot behavior from offline data and crowd-sourced annotation S Nair, E Mitchell, K Chen, B Ichter, S Savarese, C Finn Conference on Robot Learning, 1303-1315, 2022 | 163 | 2022 |
| Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation S Nair, C Finn 2020 International Conference on Learning Representations (ICLR), 2019 | 161 | 2019 |
| Reliable real‐time seismic signal/noise discrimination with machine learning MA Meier, ZE Ross, A Ramachandran, A Balakrishna, S Nair, P Kundzicz, ... Journal of Geophysical Research: Solid Earth 124 (1), 788-800, 2019 | 147 | 2019 |
| Droid: A large-scale in-the-wild robot manipulation dataset A Khazatsky, K Pertsch, S Nair, A Balakrishna, S Dasari, S Karamcheti, ... arXiv preprint arXiv:2403.12945, 2024 | 139 | 2024 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 136 | 2024 |
| Language-driven representation learning for robotics S Karamcheti, S Nair, AS Chen, T Kollar, C Finn, D Sadigh, P Liang arXiv preprint arXiv:2302.12766, 2023 | 136 | 2023 |
| Greedy hierarchical variational autoencoders for large-scale video prediction B Wu, S Nair, R Martin-Martin, L Fei-Fei, C Finn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 128 | 2021 |
| Learning generalizable robotic reward functions from" in-the-wild" human videos AS Chen, S Nair, C Finn arXiv preprint arXiv:2103.16817, 2021 | 123 | 2021 |
| Fitvid: Overfitting in pixel-level video prediction M Babaeizadeh, MT Saffar, S Nair, S Levine, C Finn, D Erhan arXiv preprint arXiv:2106.13195, 2021 | 89 | 2021 |
| Prismatic vlms: Investigating the design space of visually-conditioned language models S Karamcheti, S Nair, A Balakrishna, P Liang, T Kollar, D Sadigh Forty-first International Conference on Machine Learning, 2024 | 88 | 2024 |
| Goal-aware prediction: Learning to model what matters S Nair, S Savarese, C Finn International Conference on Machine Learning, 7207-7219, 2020 | 81 | 2020 |
| Model-Based Visual Planning with Self-Supervised Functional Distances S Tian, S Nair, F Ebert, S Dasari, B Eysenbach, C Finn, S Levine 2021 International Conference on Learning Representations (ICLR), 2020 | 68 | 2020 |
| Causal induction from visual observations for goal directed tasks S Nair, Y Zhu, S Savarese, L Fei-Fei arXiv preprint arXiv:1910.01751, 2019 | 67 | 2019 |
Chelsea FinnStanford University, Physical IntelligenceEmail được xác minh tại cs.stanford.edu