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Z. Gu, N. Boyd, and Y. Zhao, “Reactive locomotion decision-making and robust motion planning for real-time perturbation recovery,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1896–1902
1902
Earlier work this paper cites.
J. H. Park, “Impedance control for biped robot locomotion,” IEEE Transactions on Robotics and Automation , vol. 17, no. 6, pp. 870–882, 2001
2001
Earlier work this paper cites.
R. Chrisley, “Embodied artificial intelligence,” Artificial intelligence , vol. 149, no. 1, pp. 131–150, 2003
2003
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.
S. S. Mirrazavi Salehian, N. Figueroa, and A. Billard, “A unified framework for coordinated multi-arm motion planning,” The International Journal of Robotics Research , vol. 37, no. 10, pp. 1205–1232, 2018
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. Siekmann, S. Valluri, J. Dao, L. Bermillo, H. Duan, A. Fern, and J. Hurst, “Learning memory-based control for human-scale bipedal locomotion,” arXiv preprint arXiv:2006.02402 , 2020
Original
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7309–7315
2021
Earlier work this paper cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al. , “Isaac gym: High performance gpu-based physics simulation for robot learning,” arXiv preprint arXiv:2108.10470 , 2021
Original
2021
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,” arXiv preprint arXiv:2105.08328 , 2021
Original
2021
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.
A. Creswell, M. Shanahan, and I. Higgins, “Selection-inference: Exploiting large language models for interpretable logical reasoning,” arXiv preprint arXiv:2205.09712 , 2022
Original
2022
Earlier work this paper cites.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” arXiv preprint arXiv:2204.01691 , 2022
Original
2022
Earlier work this paper cites.
F. Yu, R. Batke, J. Dao, J. Hurst, K. Green, and A. Fern, “Dynamic bipedal turning through sim-to-real reinforcement learning,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 903–910
2022
Earlier work this paper cites.

Similar

G. Romualdi, S. Dafarra, G. L’Erario, I. Sorrentino, S. Traversaro, and D. Pucci, “Online non-linear centroidal mpc for humanoid robot locomotion with step adjustment,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 10 412–10 419
2022
Cited alongside, same era.
Y. Chen, T. Wu, S. Wang, X. Feng, J. Jiang, Z. Lu, S. McAleer, H. Dong, S.-C. Zhu, and Y. Yang, “Towards human-level bimanual dexterous manipulation with reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 5150–5163, 2022
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Cited alongside, same era.
Y. Avigal, L. Berscheid, T. Asfour, T. Kröger, and K. Goldberg, “Speedfolding: Learning efficient bimanual folding of garments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1–8
2022
Cited alongside, same era.
R. Gong, Q. Huang, X. Ma, H. Vo, Z. Durante, Y. Noda, Z. Zheng, S.-C. Zhu, D. Terzopoulos, L. Fei-Fei, et al. , “Mindagent: Emergent gaming interaction,” arXiv preprint arXiv:2309.09971 , 2023

Then

C. Zhang, W. Xiao, T. He, and G. Shi, “Wococo: Learning whole-body humanoid control with sequential contacts,” arXiv preprint arXiv:2406.06005 , 2024
Original
2024
Later among the works it cites.
T. He, Z. Luo, X. He, W. Xiao, C. Zhang, W. Zhang, K. Kitani, C. Liu, and G. Shi, “Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning,” arXiv preprint arXiv:2406.08858 , 2024
Original
2024
Later among the works it cites.
J. Varley, S. Singh, D. Jain, K. Choromanski, A. Zeng, S. B. R. Chowdhury, A. Dubey, and V. Sindhwani, “Embodied ai with two arms: Zero-shot learning, safety and modularity,” arXiv preprint arXiv:2404.03570 , 2024
Original
2024
Later among the works it cites.
Q. Liao, B. Zhang, X. Huang, X. Huang, Z. Li, and K. Sreenath, “Berkeley humanoid: A research platform for learning-based control,” arXiv preprint arXiv:2407.21781 , 2024
Original
2024
Later among the works it cites.

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2025

Trinity: A Modular Humanoid Robot AI System

Sun, Jingkai, Zhang, Qiang, Han, Gang et al.

Understand

In recent years, research on humanoid robots has garnered increasing attention.

With breakthroughs in various types of artificial intelligence algorithms, embodied intelligence, exemplified by humanoid robots, has been highly anticipated.
The advancements in reinforcement learning (RL) algorithms have significantly improved the motion control and generalization capabilities of humanoid robots.
Simultaneously, the groundbreaking progress in large language models (LLM) and visual language models (VLM) has brought more possibilities and imagination to humanoid robots.

Built on

Z. Gu, N. Boyd, and Y. Zhao, “Reactive locomotion decision-making and robust motion planning for real-time perturbation recovery,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1896–1902
1902
Earlier work this paper cites.
J. H. Park, “Impedance control for biped robot locomotion,” IEEE Transactions on Robotics and Automation , vol. 17, no. 6, pp. 870–882, 2001
2001
Earlier work this paper cites.
R. Chrisley, “Embodied artificial intelligence,” Artificial intelligence , vol. 149, no. 1, pp. 131–150, 2003
2003
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.
S. S. Mirrazavi Salehian, N. Figueroa, and A. Billard, “A unified framework for coordinated multi-arm motion planning,” The International Journal of Robotics Research , vol. 37, no. 10, pp. 1205–1232, 2018
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. Siekmann, S. Valluri, J. Dao, L. Bermillo, H. Duan, A. Fern, and J. Hurst, “Learning memory-based control for human-scale bipedal locomotion,” arXiv preprint arXiv:2006.02402 , 2020
Original
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7309–7315
2021
Earlier work this paper cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al. , “Isaac gym: High performance gpu-based physics simulation for robot learning,” arXiv preprint arXiv:2108.10470 , 2021
Original
2021
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,” arXiv preprint arXiv:2105.08328 , 2021
Original
2021
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.
A. Creswell, M. Shanahan, and I. Higgins, “Selection-inference: Exploiting large language models for interpretable logical reasoning,” arXiv preprint arXiv:2205.09712 , 2022
Original
2022
Earlier work this paper cites.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” arXiv preprint arXiv:2204.01691 , 2022
Original
2022
Earlier work this paper cites.
F. Yu, R. Batke, J. Dao, J. Hurst, K. Green, and A. Fern, “Dynamic bipedal turning through sim-to-real reinforcement learning,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 903–910
2022
Earlier work this paper cites.

Similar

G. Romualdi, S. Dafarra, G. L’Erario, I. Sorrentino, S. Traversaro, and D. Pucci, “Online non-linear centroidal mpc for humanoid robot locomotion with step adjustment,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 10 412–10 419
2022
Cited alongside, same era.
Y. Chen, T. Wu, S. Wang, X. Feng, J. Jiang, Z. Lu, S. McAleer, H. Dong, S.-C. Zhu, and Y. Yang, “Towards human-level bimanual dexterous manipulation with reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 5150–5163, 2022
2022
Cited alongside, same era.
Y. Avigal, L. Berscheid, T. Asfour, T. Kröger, and K. Goldberg, “Speedfolding: Learning efficient bimanual folding of garments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1–8
2022
Cited alongside, same era.
R. Gong, Q. Huang, X. Ma, H. Vo, Z. Durante, Y. Noda, Z. Zheng, S.-C. Zhu, D. Terzopoulos, L. Fei-Fei, et al. , “Mindagent: Emergent gaming interaction,” arXiv preprint arXiv:2309.09971 , 2023

Then

C. Zhang, W. Xiao, T. He, and G. Shi, “Wococo: Learning whole-body humanoid control with sequential contacts,” arXiv preprint arXiv:2406.06005 , 2024
Original
2024
Later among the works it cites.
T. He, Z. Luo, X. He, W. Xiao, C. Zhang, W. Zhang, K. Kitani, C. Liu, and G. Shi, “Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning,” arXiv preprint arXiv:2406.08858 , 2024
Original
2024
Later among the works it cites.
J. Varley, S. Singh, D. Jain, K. Choromanski, A. Zeng, S. B. R. Chowdhury, A. Dubey, and V. Sindhwani, “Embodied ai with two arms: Zero-shot learning, safety and modularity,” arXiv preprint arXiv:2404.03570 , 2024
Original
2024
Later among the works it cites.
Q. Liao, B. Zhang, X. Huang, X. Huang, Z. Li, and K. Sreenath, “Berkeley humanoid: A research platform for learning-based control,” arXiv preprint arXiv:2407.21781 , 2024
Original
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2023

Cited alongside, same era.

J. Yu, X. Wang, S. Tu, S. Cao, D. Zhang-Li, X. Lv, H. Peng, Z. Yao, X. Zhang, H. Li, et al. , “Kola: Carefully benchmarking world knowledge of large language models,” arXiv preprint arXiv:2306.09296 , 2023

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2023

Cited alongside, same era.

J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500

2023

Cited alongside, same era.

D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, et al. , “Palm-e: An embodied multimodal language model,” arXiv preprint arXiv:2303.03378 , 2023

Original

2023

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 multi-task reinforcement learning,” arXiv preprint arXiv:2302.09450 , 2023

Original

2023

Cited alongside, same era.

S. H. Jeon, S. Heim, C. Khazoom, and S. Kim, “Benchmarking potential based rewards for learning humanoid locomotion,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9204–9210

2023

Cited alongside, same era.

T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” arXiv preprint arXiv:2304.13705 , 2023

Original

2023

Cited alongside, same era.

B. Huang, Y. Chen, T. Wang, Y. Qin, Y. Yang, N. Atanasov, and X. Wang, “Dynamic handover: Throw and catch with bimanual hands,” arXiv preprint arXiv:2309.05655 , 2023

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2023

Cited alongside, same era.

Q. Zhang, P. Cui, D. Yan, J. Sun, Y. Duan, A. Zhang, and R. Xu, “Whole-body humanoid robot locomotion with human reference,” arXiv preprint arXiv:2402.18294 , 2024

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2024

Cited alongside, same era.

H. Duan, B. Pandit, M. S. Gadde, B. Van Marum, J. Dao, C. Kim, and A. Fern, “Learning vision-based bipedal locomotion for challenging terrain,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 56–62

2024