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 | 999 | 2022 |
Palm-e: An embodied multimodal language model D Driess, F Xia, MSM Sajjadi, C Lynch, A Chowdhery, B Ichter, A Wahid, ... arXiv preprint arXiv:2303.03378, 2023 | 970 | 2023 |
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 | 955 | 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 | 867 | 2020 |
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 | 590 | 2022 |
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 | 499 | 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 | 463 | 2023 |
Dynamics-aware unsupervised discovery of skills A Sharma, S Gu, S Levine, V Kumar, K Hausman arXiv preprint arXiv:1907.01657, 2019 | 417 | 2019 |
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 | 364 | 2023 |
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 | 339 | 2019 |
Learning an embedding space for transferable robot skills K Hausman, JT Springenberg, Z Wang, N Heess, M Riedmiller International Conference on Learning Representations, 2018 | 338 | 2018 |
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 | 319 | 2017 |
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 | 274 | 2023 |
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 | 245 | 2015 |
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 | 207 | 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 | 177 | 2017 |
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 | 141 | 2021 |
Scaling up multi-task robotic reinforcement learning D Kalashnikov, J Varley, Y Chebotar, B Swanson, R Jonschkowski, ... Conference on Robot Learning, 557-575, 2022 | 137 | 2022 |
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 | 135 | 2021 |
Open x-embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ... arXiv preprint arXiv:2310.08864, 2023 | 103 | 2023 |