| Palm-e: An embodied multimodal language model D Driess, F Xia, MSM Sajjadi, C Lynch, A Chowdhery, A Wahid, ... | 1766 | 2023 |
| Do as i can, not as i say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, C Fu, ... arXiv preprint arXiv:2204.01691, 2022 | 1583 | 2022 |
| Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine Conference on robot learning, 1094-1100, 2020 | 1273 | 2020 |
| Gradient surgery for multi-task learning T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn Advances in neural information processing systems 33, 5824-5836, 2020 | 1194 | 2020 |
| Rt-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... arXiv preprint arXiv:2212.06817, 2022 | 1018 | 2022 |
| Inner monologue: Embodied reasoning through planning with language models W Huang, F Xia, T Xiao, H Chan, J Liang, P Florence, A Zeng, J Tompson, ... arXiv preprint arXiv:2207.05608, 2022 | 932 | 2022 |
| Code as policies: Language model programs for embodied control J Liang, W Huang, F Xia, P Xu, K Hausman, B Ichter, P Florence, A Zeng 2023 IEEE International Conference on Robotics and Automation (ICRA), 9493-9500, 2023 | 910 | 2023 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... arXiv preprint arXiv:2307.15818, 2023 | 886 | 2023 |
| Open x-embodiment: Robotic learning datasets and rt-x models JJ Lim IEEE International Conference on Robotics and Automation, 2024 | 535* | 2024 |
| Do as i can, not as i say: Grounding language in robotic affordances A Brohan, Y Chebotar, C Finn, K Hausman, A Herzog, D Ho, J Ibarz, ... Conference on robot learning, 287-318, 2023 | 523 | 2023 |
| Dynamics-aware unsupervised discovery of skills A Sharma, S Gu, S Levine, V Kumar, K Hausman arXiv preprint arXiv:1907.01657, 2019 | 502 | 2019 |
| Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning A Gupta, V Kumar, C Lynch, S Levine, K Hausman arXiv preprint arXiv:1910.11956, 2019 | 468 | 2019 |
| Interactive perception: Leveraging action in perception and perception in action J Bohg, K Hausman, B Sankaran, O Brock, D Kragic, S Schaal, ... IEEE Transactions on Robotics 33 (6), 1273-1291, 2017 | 368 | 2017 |
| Learning an embedding space for transferable robot skills K Hausman, JT Springenberg, Z Wang, N Heess, M Riedmiller International Conference on Learning Representations, 2018 | 361 | 2018 |
| Mt-opt: Continuous multi-task robotic reinforcement learning at scale D Kalashnikov, J Varley, Y Chebotar, B Swanson, R Jonschkowski, ... arXiv preprint arXiv:2104.08212, 2021 | 299* | 2021 |
| Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor Z Su, K Hausman, Y Chebotar, A Molchanov, GE Loeb, GS Sukhatme, ... 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids …, 2015 | 272 | 2015 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023 | 229 | 2023 |
| Combining model-based and model-free updates for trajectory-centric reinforcement learning Y Chebotar, K Hausman, M Zhang, G Sukhatme, S Schaal, S Levine International conference on machine learning, 703-711, 2017 | 224 | 2017 |
| Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets K Hausman, Y Chebotar, S Schaal, G Sukhatme, JJ Lim Advances in neural information processing systems 30, 2017 | 198 | 2017 |
| Actionable models: Unsupervised offline reinforcement learning of robotic skills Y Chebotar, K Hausman, Y Lu, T Xiao, D Kalashnikov, J Varley, A Irpan, ... arXiv preprint arXiv:2104.07749, 2021 | 167 | 2021 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
