Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains

L.-J. Lin, “Self-improving reactive agents based on reinforcement learning, planning and teaching,” Machine learning , vol. 8, pp. 293–321, 1992

1992

Earlier work this paper cites.

S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the International Conference on Artificial Intelligence and Statistics , 2011, pp. 627–635

2011

Earlier work this paper cites.

J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1238–1274, 2013

2013

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. 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.

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.

T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning . PMLR, 2018, pp. 1861–1870

2018

Earlier work this paper cites.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data , vol. 6, no. 1, pp. 1–48, 2019

2019

Earlier work this paper cites.

D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” in Conference on Robot Learning , 2020, pp. 66–75

2020

Earlier work this paper cites.

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

Earlier work this paper cites.

J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind bipedal stair traversal via sim-to-real reinforcement learning,” in Robotics: Science and Systems , 2021

2021

Earlier work this paper cites.

L. Weihs, U. Jain, I.-J. Liu, J. Salvador, S. Lazebnik, A. Kembhavi, and A. Schwing, “Bridging the imitation gap by adaptive insubordination,” Advances in Neural Information Processing Systems , vol. 34, pp. 19 134–19 146, 2021

2021

Earlier work this paper cites.

T. Ni, B. Eysenbach, and R. Salakhutdinov, “Recurrent model-free rl can be a strong baseline for many pomdps,” arXiv preprint arXiv:2110.05038 , 2021

Original

2021

Earlier work this paper cites.

T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022

2022

Cited alongside, same era.

Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Robust and versatile bipedal jumping control through reinforcement learning,” in Robotics: Science and Systems , 2023

2023

Cited alongside, same era.

T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, M. Wulfmeier, J. Humplik, S. Tunyasuvunakool, N. Y. Siegel, R. Hafner, et al. , “Learning agile soccer skills for a bipedal robot with deep reinforcement learning,” preprint arXiv:2304.13653 , 2023

Original

2023

Cited alongside, same era.

M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg, “Orbit: A unified simulation framework for interactive robot learning environments,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3740–3747, 2023

P. Dugar, A. Shrestha, F. Yu, B. van Marum, and A. Fern, “Learning multi-modal whole-body control for real-world humanoid robots,” arXiv preprint arXiv:2408.07295 , 2024

Original

2024

Closest in time.

X. Gu, Y.-J. Wang, and J. Chen, “Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer,” arXiv preprint arXiv:2404.05695 , 2024

Original

2024

Closest in time.

J. Long, J. Ren, M. Shi, Z. Wang, T. Huang, P. Luo, and J. Pang, “Learning humanoid locomotion with perceptive internal model,” arXiv preprint arXiv:2411.14386 , 2024

Original

2024

Closest in time.

F. Wu, Z. Gu, H. Wu, A. Wu, and Y. Zhao, “Infer and adapt: Bipedal locomotion reward learning from demonstrations via inverse reinforcement learning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 16 243–16 250

2024

Closest in time.