Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 5026–5033

2012

Earlier work this paper cites.

M. T. Spaan, “Partially observable markov decision processes,” in Reinforcement learning: State-of-the-art . Springer, 2012, pp. 387–414

2012

Earlier work this paper cites.

J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel, “High-dimensional continuous control using generalized advantage estimation,” arXiv preprint arXiv:1506.02438 , 2015

Original

2015

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Earlier work this paper cites.

L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel, “Asymmetric actor critic for image-based robot learning,” arXiv preprint arXiv:1710.06542 , 2017

Original

2017

Earlier work this paper cites.

J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30

2017

Earlier work this paper cites.

X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 3803–3810

2018

Earlier work this paper cites.

J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” arXiv preprint arXiv:1804.10332 , 2018

Original

2018

Earlier work this paper cites.

J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019

2019

Earlier work this paper cites.

W. Zhao, J. P. Queralta, and T. Westerlund, “Sim-to-real transfer in deep reinforcement learning for robotics: a survey,” in 2020 IEEE symposium series on computational intelligence (SSCI) . IEEE, 2020, pp. 737–744

2020

Cited alongside, same era.

A. Kadian, J. Truong, A. Gokaslan, A. Clegg, E. Wijmans, S. Lee, M. Savva, S. Chernova, and D. Batra, “Sim2real predictivity: Does evaluation in simulation predict real-world performance?” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6670–6677, 2020

2020

Cited alongside, same era.

J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020

2020

Cited alongside, same era.

A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034 , 2021

Original

2021

Cited alongside, same era.

G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” The International Journal of Robotics Research , p. 02783649231224053, 2022

2022

Later among the works it cites.

A. Kumar, Z. Li, J. Zeng, D. Pathak, K. Sreenath, and J. Malik, “Adapting rapid motor adaptation for bipedal robots,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1161–1168

2022

Later among the works it cites.

Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots, “Fast and efficient locomotion via learned gait transitions,” in Conference on robot learning . PMLR, 2022, pp. 773–783

2022

Later among the works it cites.

Y. Guo, Z. Jiang, Y.-J. Wang, J. Gao, and J. Chen, “Decentralized motor skill learning for complex robotic systems,” IEEE Robotics and Automation Letters , 2023

2023

Later among the works it cites.