| 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 | 635 | 2022 |
| 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 | 615* | 2023 |
| Open X-Embodiment: Robotic learning datasets and RT-X models OXE Collaboration, A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, ... arXiv preprint arXiv:2310.08864, 2023 | 252* | 2023 |
| Accelerating Reinforcement Learning with Learned Skill Priors K Pertsch, Y Lee, JJ Lim Conference on Robot Learning (CoRL), 2020, 2020 | 240 | 2020 |
| Demonstration-Guided Reinforcement Learning with Learned Skills K Pertsch, Y Lee, Y Wu, JJ Lim Conference on Robot Learning (CoRL), 2021, 2021 | 79* | 2021 |
| Octo: An open-source generalist robot policy OM Team, D Ghosh, H Walke, K Pertsch, K Black, O Mees, S Dasari, ... arXiv preprint arXiv:2405.12213, 2024 | 78 | 2024 |
| iPose: instance-aware 6D pose estimation of partly occluded objects OH Jafari*, SK Mustikovela*, K Pertsch, E Brachmann, C Rother Asian Conference on Computer Vision (ACCV), 2018, 2017 | 75* | 2017 |
| Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors K Pertsch, O Rybkin, F Ebert, C Finn, D Jayaraman, S Levine Conference on Neural Information Processing Systems (NeurIPS), 2020, 2020 | 72 | 2020 |
| Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments J Yamada, Y Lee, G Salhotra, K Pertsch, M Pflueger, GS Sukhatme, ... Conference on Robot Learning (CoRL), 2020, 2020 | 63 | 2020 |
| Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions Y Chebotar, Q Vuong, K Hausman, F Xia, Y Lu, A Irpan, A Kumar, T Yu, ... Conference on Robot Learning, 3909-3928, 2023 | 55 | 2023 |
| Skill-based Meta-Reinforcement Learning T Nam, SH Sun, K Pertsch, SJ Hwang, JJ Lim International Conference on Learning Representations (ICLR), 2022, 2022 | 47 | 2022 |
| Bootstrap your own skills: Learning to solve new tasks with large language model guidance J Zhang, J Zhang, K Pertsch, Z Liu, X Ren, M Chang, SH Sun, JJ Lim arXiv preprint arXiv:2310.10021, 2023 | 37 | 2023 |
| Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning K Pertsch, O Rybkin, J Yang, K Derpanis, K Daniilidis, J Lim, A Jaegle 2nd Conference on Learning for Dynamics and Control (L4DC), 2020, 2020 | 31* | 2020 |
| Learning what you can do before doing anything O Rybkin*, K Pertsch*, KG Derpanis, K Daniilidis, A Jaegle International Conference on Learning Representations (ICLR), 2019, 2018 | 31* | 2018 |
| Roboclip: One demonstration is enough to learn robot policies S Sontakke, J Zhang, S Arnold, K Pertsch, E Bıyık, D Sadigh, C Finn, L Itti Advances in Neural Information Processing Systems 36, 2024 | 28 | 2024 |
| 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 | 26 | 2024 |
| 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 | 25 | 2024 |
| Yell At Your Robot: Improving On-the-Fly from Language Corrections L Xiaoyang Shi, Z Hu, TZ Zhao, A Sharma, K Pertsch, J Luo, S Levine, ... arXiv e-prints, arXiv: 2403.12910, 2024 | 17* | 2024 |
| PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection S Dass, K Pertsch, H Zhang, Y Lee, JJ Lim, S Nikolaidis arXiv preprint arXiv:2212.04708, 2022 | 13 | 2022 |
| Transformer adapters for robot learning A Liang, I Singh, K Pertsch, J Thomason CoRL 2022 Workshop on Pre-training Robot Learning, 2022 | 12 | 2022 |
Joseph J. LimAssociate Professor at KAISTEmail verificata su csail.mit.edu
