| 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 | 1853 | 2018 |
| 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 | 417 | 2018 |
| Dart: Dynamic animation and robotics toolkit J Lee, M X. Grey, S Ha, T Kunz, S Jain, Y Ye, S S. Srinivasa, M Stilman, ... The Journal of Open Source Software 3 (22), 500, 2018 | 269 | 2018 |
| 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 | 129 | 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 | 102 | 2020 |
| Joint Optimization of Robot Design and Motion Parameters using the Implicit Function Theorem. S Ha, S Coros, A Alspach, J Kim, K Yamane Robotics: Science and systems 8, 2017 | 73 | 2017 |
| Soft actor-critic algorithms and applications. arXiv 2018 T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 1812 | 70 | 1812 |
| Computational co-optimization of design parameters and motion trajectories for robotic systems S Ha, S Coros, A Alspach, J Kim, K Yamane The International Journal of Robotics Research 37 (13-14), 1521-1536, 2018 | 68 | 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 | 67 | 2020 |
| Computational design of robotic devices from high-level motion specifications S Ha, S Coros, A Alspach, JM Bern, J Kim, K Yamane IEEE Transactions on Robotics 34 (5), 1240-1251, 2018 | 58 | 2018 |
| Iterative training of dynamic skills inspired by human coaching techniques S Ha, CK Liu ACM Transactions on Graphics (TOG) 34 (1), 1-11, 2014 | 54 | 2014 |
| Legged robots that keep on learning: Fine-tuning locomotion policies in the real world L Smith, JC Kew, XB Peng, S Ha, J Tan, S Levine 2022 International Conference on Robotics and Automation (ICRA), 1593-1599, 2022 | 51 | 2022 |
| Falling and landing motion control for character animation S Ha, Y Ye, CK Liu ACM Transactions on Graphics (TOG) 31 (6), 1-9, 2012 | 51 | 2012 |
| Human motion reconstruction from force sensors S Ha, Y Bai, CK Liu Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer …, 2011 | 48 | 2011 |
| Automated deep reinforcement learning environment for hardware of a modular legged robot S Ha, J Kim, K Yamane 2018 15th international conference on ubiquitous robots (UR), 348-354, 2018 | 44 | 2018 |
| Multiple contact planning for minimizing damage of humanoid falls S Ha, CK Liu 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 44 | 2015 |
| Visual-locomotion: Learning to walk on complex terrains with vision W Yu, D Jain, A Escontrela, A Iscen, P Xu, E Coumans, S Ha, J Tan, ... 5th Annual Conference on Robot Learning, 2021 | 43 | 2021 |
| Task-based limb optimization for legged robots S Ha, S Coros, A Alspach, J Kim, K Yamane 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 43 | 2016 |
| Pods: Policy optimization via differentiable simulation MAZ Mora, M Peychev, S Ha, M Vechev, S Coros International Conference on Machine Learning, 7805-7817, 2021 | 23 | 2021 |
| Reducing hardware experiments for model learning and policy optimization S Ha, K Yamane 2015 IEEE International Conference on Robotics and Automation (ICRA), 2620-2626, 2015 | 23 | 2015 |
