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Zhanpeng He
Zhanpeng He
Verified email at cs.columbia.edu - Homepage
Title
Cited by
Cited by
Year
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
4292020
Learning 3d dynamic scene representations for robot manipulation
Z Xu, Z He, J Wu, S Song
arXiv preprint arXiv:2011.01968, 2020
262020
Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning
T Chen, Z He, M Ciocarlie
arXiv preprint arXiv:2008.04460, 2020
202020
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
152018
Universal Manipulation Policy Network for Articulated Objects
Z Xu, Z He, S Song
IEEE Robotics and Automation Letters 7 (2), 2447-2454, 2022
142022
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
11*2020
Meta-world: A 456 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 158, 1094-1100, 2019
112019
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
62018
Meta-world: a benchmark and evaluation for multi-task and meta-reinforcement learning (2019)
T Yu, D Quillen, Z He, R Julian, K Hausman, S Levine, C Finn
URL https://github. com/rlworkgroup/metaworld, 0
6
Co-designing hardware and control for robot hands
T Chen, Z He, M Ciocarlie
Science Robotics 6 (54), eabg2133, 2021
32021
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
32020
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
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
12022
UMPNet: Universal manipulation policy network for articulated objects
Z Xu, Z He, S Song
arXiv preprint arXiv:2109.05668, 2021
12021
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