Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations

Chengtian Ma, Yunyue Wei, Chenhui Zuo, Chen Zhang, Yanan Sui
Proceedings of The 9th Conference on Robot Learning, PMLR 305:4641-4656, 2025.

Abstract

Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of static balance and falling remains limited. This work presents a hierarchical control pipeline for simulating human balance via a comprehensive whole-body musculoskeletal system. We identified spatiotemporal dynamics of balancing during stable standing, revealed the impact of muscle injury on balancing behavior, and generated fall contact patterns that aligned with clinical data. Furthermore, our simulated hip exoskeleton assistance demonstrated improvement in balance maintenance and reduced muscle effort under perturbation. This work offers unique muscle-level insights into human balance dynamics that are challenging to capture experimentally. It could provide a foundation for developing targeted interventions for individuals with balance impairments and support the advancement of humanoid robotic systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-ma25d, title = {Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations}, author = {Ma, Chengtian and Wei, Yunyue and Zuo, Chenhui and Zhang, Chen and Sui, Yanan}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {4641--4656}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/ma25d/ma25d.pdf}, url = {https://proceedings.mlr.press/v305/ma25d.html}, abstract = {Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of static balance and falling remains limited. This work presents a hierarchical control pipeline for simulating human balance via a comprehensive whole-body musculoskeletal system. We identified spatiotemporal dynamics of balancing during stable standing, revealed the impact of muscle injury on balancing behavior, and generated fall contact patterns that aligned with clinical data. Furthermore, our simulated hip exoskeleton assistance demonstrated improvement in balance maintenance and reduced muscle effort under perturbation. This work offers unique muscle-level insights into human balance dynamics that are challenging to capture experimentally. It could provide a foundation for developing targeted interventions for individuals with balance impairments and support the advancement of humanoid robotic systems.} }
Endnote
%0 Conference Paper %T Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations %A Chengtian Ma %A Yunyue Wei %A Chenhui Zuo %A Chen Zhang %A Yanan Sui %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-ma25d %I PMLR %P 4641--4656 %U https://proceedings.mlr.press/v305/ma25d.html %V 305 %X Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of static balance and falling remains limited. This work presents a hierarchical control pipeline for simulating human balance via a comprehensive whole-body musculoskeletal system. We identified spatiotemporal dynamics of balancing during stable standing, revealed the impact of muscle injury on balancing behavior, and generated fall contact patterns that aligned with clinical data. Furthermore, our simulated hip exoskeleton assistance demonstrated improvement in balance maintenance and reduced muscle effort under perturbation. This work offers unique muscle-level insights into human balance dynamics that are challenging to capture experimentally. It could provide a foundation for developing targeted interventions for individuals with balance impairments and support the advancement of humanoid robotic systems.
APA
Ma, C., Wei, Y., Zuo, C., Zhang, C. & Sui, Y.. (2025). Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:4641-4656 Available from https://proceedings.mlr.press/v305/ma25d.html.

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