| Open X-Embodiment: Robotic learning datasets and RT-X models OXE Collaboration, A O’Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, ... arXiv preprint arXiv:2310.08864 1 (2), 2023 | 1422* | 2023 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| Learning modular neural network policies for multi-task and multi-robot transfer C Devin, A Gupta, T Darrell, P Abbeel, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 2169-2176, 2017 | 523 | 2017 |
| Learning invariant feature spaces to transfer skills with reinforcement learning A Gupta, C Devin, YX Liu, P Abbeel, S Levine arXiv preprint arXiv:1703.02949, 2017 | 388 | 2017 |
| Learning to reach goals via iterated supervised learning D Ghosh, A Gupta, A Reddy, J Fu, C Devin, B Eysenbach, S Levine arXiv preprint arXiv:1912.06088, 2019 | 256 | 2019 |
| Gemini robotics: Bringing ai into the physical world GR Team, S Abeyruwan, J Ainslie, JB Alayrac, MG Arenas, T Armstrong, ... arXiv preprint arXiv:2503.20020, 2025 | 206 | 2025 |
| Robocat: A self-improving foundation agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauza, T Davchev, ... arXiv preprint arXiv:2306.11706 1 (8), 2023 | 205* | 2023 |
| Adapting deep visuomotor representations with weak pairwise constraints E Tzeng, C Devin, J Hoffman, C Finn, P Abbeel, S Levine, K Saenko, ... arXiv preprint arXiv:1511.07111, 2015 | 187* | 2015 |
| Deep object-centric policies for autonomous driving D Wang, C Devin, QZ Cai, F Yu, T Darrell 2019 International Conference on Robotics and Automation (ICRA), 8853-8859, 2019 | 150 | 2019 |
| Deep object-centric representations for generalizable robot learning C Devin, P Abbeel, T Darrell, S Levine 2018 IEEE International Conference on Robotics and Automation (ICRA), 7111-7118, 2018 | 142 | 2018 |
| Grasp2vec: Learning object representations from self-supervised grasping E Jang, C Devin, V Vanhoucke, S Levine arXiv preprint arXiv:1811.06964, 2018 | 139 | 2018 |
| Beyond pick-and-place: Tackling robotic stacking of diverse shapes AX Lee, CM Devin, Y Zhou, T Lampe, K Bousmalis, JT Springenberg, ... 5th Annual Conference on Robot Learning, 2021 | 131 | 2021 |
| Towards adapting deep visuomotor representations from simulated to real environments E Tzeng, C Devin, J Hoffman, C Finn, X Peng, S Levine, K Saenko, ... arXiv preprint arXiv:1511.07111 2 (3), 2015 | 123 | 2015 |
| Monocular plan view networks for autonomous driving D Wang, C Devin, QZ Cai, P Krähenbühl, T Darrell 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 100 | 2019 |
| Fully autonomous real-world reinforcement learning with applications to mobile manipulation C Sun, J Orbik, CM Devin, BH Yang, A Gupta, G Berseth, S Levine Conference on Robot Learning, 308-319, 2022 | 77 | 2022 |
| Embedding word similarity with neural machine translation F Hill, K Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1412.6448, 2014 | 70 | 2014 |
| Smirl: Surprise minimizing reinforcement learning in unstable environments G Berseth, D Geng, C Devin, N Rhinehart, C Finn, D Jayaraman, S Levine arXiv preprint arXiv:1912.05510, 2019 | 62 | 2019 |
| Not all neural embeddings are born equal F Hill, KH Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1410.0718, 2014 | 54 | 2014 |
| Modular networks for compositional instruction following R Corona, D Fried, C Devin, D Klein, T Darrell Proceedings of the 2021 conference of the north american chapter of the …, 2021 | 39 | 2021 |
| Learning to reach goals without reinforcement learning D Ghosh, A Gupta, J Fu, A Reddy, C Devin, B Eysenbach, S Levine | 33 | 2019 |
