Jie Tan
Cited by
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Soft actor-critic algorithms and applications
T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ...
arXiv preprint arXiv:1812.05905, 2018
Large-scale evolution of image classifiers
E Real, S Moore, A Selle, S Saxena, YL Suematsu, J Tan, Q Le, A Kurakin
International Conference on Machine Learning (ICML), 2017
Sim-to-real: Learning agile locomotion for quadruped robots
J Tan, T Zhang, E Coumans, A Iscen, Y Bai, D Hafner, S Bohez, ...
arXiv preprint arXiv:1804.10332, 2018
How to train your robot with deep reinforcement learning: lessons we have learned
J Ibarz, J Tan, C Finn, M Kalakrishnan, P Pastor, S Levine
The International Journal of Robotics Research 40 (4-5), 698-721, 2021
Learning to walk via deep reinforcement learning
T Haarnoja, S Ha, A Zhou, J Tan, G Tucker, S Levine
arXiv preprint arXiv:1812.11103, 2018
Learning agile robotic locomotion skills by imitating animals
XB Peng, E Coumans, T Zhang, TW Lee, J Tan, S Levine
arXiv preprint arXiv:2004.00784, 2020
Adaptive power system emergency control using deep reinforcement learning
Q Huang, R Huang, W Hao, J Tan, R Fan, Z Huang
IEEE Transactions on Smart Grid 11 (2), 1171-1182, 2019
Preparing for the unknown: Learning a universal policy with online system identification
W Yu, J Tan, CK Liu, G Turk
Robotics: Science and Systems (RSS), 2017
Learning to walk in the real world with minimal human effort
S Ha, P Xu, Z Tan, S Levine, J Tan
arXiv preprint arXiv:2002.08550, 2020
Language to rewards for robotic skill synthesis
W Yu, N Gileadi, C Fu, S Kirmani, KH Lee, MG Arenas, HTL Chiang, ...
arXiv preprint arXiv:2306.08647, 2023
On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward
HS Choi, C Crump, C Duriez, A Elmquist, G Hager, D Han, F Hearl, ...
Proceedings of the National Academy of Sciences 118 (1), e1907856118, 2021
Data efficient reinforcement learning for legged robots
Y Yang, K Caluwaerts, A Iscen, T Zhang, J Tan, V Sindhwani
Conference on Robot Learning, 1-10, 2020
Learning to be safe: Deep rl with a safety critic
K Srinivasan, B Eysenbach, S Ha, J Tan, C Finn
arXiv preprint arXiv:2010.14603, 2020
Policies modulating trajectory generators
A Iscen, K Caluwaerts, J Tan, T Zhang, E Coumans, V Sindhwani, ...
Conference on Robot Learning, 916-926, 2018
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
Stable proportional-derivative controllers
J Tan, K Liu, G Turk
IEEE Computer Graphics and Applications 31 (4), 34-44, 2011
Articulated swimming creatures
J Tan, Y Gu, G Turk, CK Liu
ACM Transactions on Graphics (TOG) 30 (4), 1-12, 2011
Learning bicycle stunts
J Tan, Y Gu, CK Liu, G Turk
ACM Transactions on Graphics (TOG) 33 (4), 1-12, 2014
Learning to dress: Synthesizing human dressing motion via deep reinforcement learning
A Clegg, W Yu, J Tan, CK Liu, G Turk
ACM Transactions on Graphics (TOG) 37 (6), 1-10, 2018
Learning fast adaptation with meta strategy optimization
W Yu, J Tan, Y Bai, E Coumans, S Ha
IEEE Robotics and Automation Letters 5 (2), 2950-2957, 2020
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