| 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 | 957 | 2020 |
| Universal manipulation policy network for articulated objects Z Xu, Z He, S Song IEEE robotics and automation letters 7 (2), 2447-2454, 2022 | 69 | 2022 |
| Learning 3d dynamic scene representations for robot manipulation Z Xu, Z He, J Wu, S Song arXiv preprint arXiv:2011.01968, 2020 | 48 | 2020 |
| Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning T Chen, Z He, M Ciocarlie arXiv preprint arXiv:2008.04460, 2020 | 42 | 2020 |
| Zero-shot skill composition and simulation-to-real transfer by learning task representations Z He, R Julian, E Heiden, H Zhang, S Schaal, JJ Lim, G Sukhatme, ... arXiv preprint arXiv:1810.02422, 2018 | 20 | 2018 |
| Scaling simulation-to-real transfer by learning a latent space of robot skills RC Julian, E Heiden, Z He, H Zhang, S Schaal, JJ Lim, GS Sukhatme, ... The International Journal of Robotics Research 39 (10-11), 1259-1278, 2020 | 19* | 2020 |
| Squirl: Robust and efficient learning from video demonstration of long-horizon robotic manipulation tasks B Wu, F Xu, Z He, A Gupta, PK Allen 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 14 | 2020 |
| Meta-world: A 352 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 353, 1094-1100, 0 | 13 | |
| Co-designing hardware and control for robot hands T Chen, Z He, M Ciocarlie Science Robotics 6 (54), eabg2133, 2021 | 9 | 2021 |
| Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills H Zhang, E Heiden, R Julian, Z He, S Schaal, J Lim, G Sukhatme arXiv preprint arXiv:1810.00146, 2018 | 9 | 2018 |
| Meta-world: a benchmark and evaluation for multi-task and meta-reinforcement learning (2019) T Yu, D Quillen, Z He, R Julian, A Narayan, H Shively, A Bellathur, ... URL https://github. com/rlworkgroup/metaworld, 1910 | 8 | 1910 |
| Pick2place: Task-aware 6dof grasp estimation via object-centric perspective affordance Z He, N Chavan-Dafle, J Huh, S Song, V Isler 2023 IEEE International Conference on Robotics and Automation (ICRA), 7996-8002, 2023 | 7 | 2023 |
| Decision making for human-in-the-loop robotic agents via uncertainty-aware reinforcement learning S Singi, Z He, A Pan, S Patel, GA Sigurdsson, R Piramuthu, S Song, ... arXiv preprint arXiv:2303.06710, 2023 | 5 | 2023 |
| Discovering synergies for robot manipulation with multi-task reinforcement learning Z He, M Ciocarlie 2022 International Conference on Robotics and Automation (ICRA), 2714-2721, 2022 | 5 | 2022 |
| 447 Sergey Levine. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement 448 learning T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn Conference on Robot Learning, 1094-1100, 0 | 3 | |
| MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy Z He, M Ciocarlie arXiv preprint arXiv:2309.17227, 2023 | 1 | 2023 |
| Task-Based Design and Policy Co-Optimization for Tendon-driven Underactuated Kinematic Chains S Islam, Z He, M Ciocarlie arXiv preprint arXiv:2405.14566, 2024 | | 2024 |
Ryan C. JulianGoogle DeepMind在 google.com 的电子邮件经过验证
