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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.
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.
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.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” Advances in neural information processing systems , 2020
2020
Earlier work this paper cites.
W. Xu and F. Zhang, “Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3317–3324, 2021
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
Earlier work this paper cites.
T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “Elevation mapping for locomotion and navigation using gpu,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 2273–2280
2022
Earlier work this paper cites.
W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar-inertial odometry,” IEEE Transactions on Robotics , vol. 38, no. 4, pp. 2053–2073, 2022
2022
Earlier work this paper cites.
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
Earlier work this paper cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on robot learning . PMLR, 2023, pp. 403–415
2023
Earlier work this paper cites.

Similar

J. Long, Z. Wang, Q. Li, L. Cao, J. Gao, and J. Pang, “Hybrid internal model: Learning agile legged locomotion with simulated robot response,” in The Twelfth International Conference on Learning Representations , 2024
2024
Cited alongside, same era.
J. Long, W. Yu, Q. Li, Z. Wang, D. Lin, and J. Pang, “Learning h-infinity locomotion control,” in 8th Annual Conference on Robot Learning , 2024
2024
Cited alongside, same era.
X. Gu, Y.-J. Wang, X. Zhu, C. Shi, Y. Guo, Y. Liu, and J. Chen, “Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning,” arXiv preprint arXiv:2408.14472 , 2024
Original
2024
Cited alongside, same era.
I. Radosavovic, B. Zhang, B. Shi, J. Rajasegaran, S. Kamat, T. Darrell, K. Sreenath, and J. Malik, “Humanoid locomotion as next token prediction,” arXiv preprint arXiv:2402.19469 , 2024
Original
2024
Cited alongside, same era.

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2024

Learning Humanoid Locomotion with Perceptive Internal Model

Long, Junfeng, Ren, Junli, Shi, Moji et al.

Understand

In contrast to quadruped robots that can navigate diverse terrains using a "blind" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology.

However, incorporating perceptual signals often introduces additional disturbances to the system, potentially reducing its robustness, generalizability, and efficiency.
This paper presents the Perceptive Internal Model (PIM), which relies on onboard, continuously updated elevation maps centered around the robot to perceive its surroundings.
We train the policy using ground-truth obstacle heights surrounding the robot in simulation, optimizing it based on the Hybrid Internal Model (HIM), and perform inference with heights sampled from the constructed elevation map.

Built on

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.
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.
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.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” Advances in neural information processing systems , 2020
2020
Earlier work this paper cites.
W. Xu and F. Zhang, “Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3317–3324, 2021
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
Earlier work this paper cites.
T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “Elevation mapping for locomotion and navigation using gpu,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 2273–2280
2022
Earlier work this paper cites.
W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar-inertial odometry,” IEEE Transactions on Robotics , vol. 38, no. 4, pp. 2053–2073, 2022
2022
Earlier work this paper cites.
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
Earlier work this paper cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on robot learning . PMLR, 2023, pp. 403–415
2023
Earlier work this paper cites.

Similar

J. Long, Z. Wang, Q. Li, L. Cao, J. Gao, and J. Pang, “Hybrid internal model: Learning agile legged locomotion with simulated robot response,” in The Twelfth International Conference on Learning Representations , 2024
2024
Cited alongside, same era.
J. Long, W. Yu, Q. Li, Z. Wang, D. Lin, and J. Pang, “Learning h-infinity locomotion control,” in 8th Annual Conference on Robot Learning , 2024
2024
Cited alongside, same era.
X. Gu, Y.-J. Wang, X. Zhu, C. Shi, Y. Guo, Y. Liu, and J. Chen, “Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning,” arXiv preprint arXiv:2408.14472 , 2024
Original
2024
Cited alongside, same era.
I. Radosavovic, B. Zhang, B. Shi, J. Rajasegaran, S. Kamat, T. Darrell, K. Sreenath, and J. Malik, “Humanoid locomotion as next token prediction,” arXiv preprint arXiv:2402.19469 , 2024
Original
2024
Cited alongside, same era.

Then

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

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X. Cheng, Y. Ji, J. Chen, R. Yang, G. Yang, and X. Wang, “Expressive whole-body control for humanoid robots,” arXiv preprint arXiv:2402.16796 , 2024

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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

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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

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J. Li, Y. Zhu, Y. Xie, Z. Jiang, M. Seo, G. Pavlakos, and Y. Zhu, “Okami: Teaching humanoid robots manipulation skills through single video imitation,” in 8th Annual Conference on Robot Learning

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C. Zhang, W. Xiao, T. He, and G. Shi, “Wococo: Learning whole-body humanoid control with sequential contacts,” arXiv preprint arXiv:2406.06005 , 2024

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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

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