Fetching the paper…
Reading the bibliography…
Humanoid robots have demonstrated robust locomotion capabilities using Reinforcement Learning (RL)-based approaches.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation , vol. 3, no. 1, pp. 79–87, 1991
1991
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions On Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Mahmood, N. Ghorbani, N. F. Troje, G. Pons-Moll, and M. J. Black, “Amass: Archive of motion capture as surface shapes,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 5442–5451
2019
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.
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” IEEE Transactions on Robotics , vol. 36, no. 4, pp. 1307–1319, 2020
2020
Earlier work this paper cites.
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient surgery for multi-task learning,” Advances in neural information processing systems , vol. 33, pp. 5824–5836, 2020
2020
Earlier work this paper cites.
F. G. Harvey, M. Yurick, D. Nowrouzezahrai, and C. Pal, “Robust motion in-betweening,” vol. 39, no. 4, 2020
2020
Earlier work this paper cites.
X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: Adversarial motion priors for stylized physics-based character control,” ACM Transactions on Graphics (ToG) , vol. 40, no. 4, pp. 1–20, 2021
2021
Earlier work this paper cites.
S. Sodhani, A. Zhang, and J. Pineau, “Multi-task reinforcement learning with context-based representations,” in International Conference on Machine Learning . PMLR, 2021, pp. 9767–9779
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
B. Liu, X. Liu, X. Jin, P. Stone, and Q. Liu, “Conflict-averse gradient descent for multi-task learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 18 878–18 890, 2021
2021
Cited alongside, same era.
A. Escontrela, X. B. Peng, W. Yu, T. Zhang, A. Iscen, K. Goldberg, and P. Abbeel, “Adversarial motion priors make good substitutes for complex reward functions,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 25–32
2022
Cited alongside, same era.
Z. Chen, Y. Deng, Y. Wu, Q. Gu, and Y. Li, “Towards understanding the mixture-of-experts layer in deep learning,” Advances in neural information processing systems , vol. 35, pp. 23 049–23 062, 2022
2022
Cited alongside, same era.
S. Zhou, W. Zhang, J. Jiang, W. Zhong, J. Gu, and W. Zhu, “On the convergence of stochastic multi-objective gradient manipulation and beyond,” Advances in Neural Information Processing Systems , vol. 35, pp. 38 103–38 115, 2022
2022
2024
Later among the works it cites.
A. Tang, T. Hiraoka, N. Hiraoka, F. Shi, K. Kawaharazuka, K. Kojima, K. Okada, and M. Inaba, “Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 13 107–13 114
2024
Later among the works it cites.
Q. Zhang, P. Cui, D. Yan, J. Sun, Y. Duan, G. Han, W. Zhao, W. Zhang, Y. Guo, A. Zhang et al. , “Whole-body humanoid robot locomotion with human reference,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 11 225–11 231
2024
Later among the works it cites.
I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath, “Real-world humanoid locomotion with reinforcement learning,” Science Robotics , vol. 9, no. 89, p. eadi9579, 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100
2022
Cited alongside, same era.
J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4975–4982, 2023
2023
Cited alongside, same era.
G. B. Margolis and P. Agrawal, “Walk these ways: Tuning robot control for generalization with multiplicity of behavior,” in Conference on Robot Learning . PMLR, 2023, pp. 22–31
2023
Cited alongside, same era.
S. Luo, S. Li, R. Yu, Z. Wang, J. Wu, and Q. Zhu, “Pie: Parkour with implicit-explicit learning framework for legged robots,” IEEE Robotics and Automation Letters , 2024
2024
Cited alongside, same era.
W. Cui, S. Li, H. Huang, B. Qin, T. Zhang, L. Zheng, Z. Tang, C. Hu, N. Yan, J. Chen et al. , “Adapting humanoid locomotion over challenging terrain via two-phase training,” in 8th Annual Conference on Robot Learning , 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Z. Zhuang, S. Yao, and H. Zhao, “Humanoid parkour learning,” arXiv preprint arXiv:2406.10759 , 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
2025
Closest in time.
H. Wang, Z. Wang, J. Ren, Q. Ben, T. Huang, W. Zhang, and J. Pang, “Beamdojo: Learning agile humanoid locomotion on sparse footholds,” in Robotics: Science and Systems (RSS) , 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.