Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 68 references that are in the map. First posted on arXiv 11 Jun 2025.
Builds on
12 of 68 references.
References · 12
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2022MyoSuite -- A contact-rich simulation suite for musculoskeletal motor controlcited by 80
- 2022Predictive Sampling: Real-time Behaviour Synthesis with MuJoCocited by 47
- 2022DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systemscited by 29
- 2023SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representationcited by 17
- 2023Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representationcited by 17
- 2023MuscleVAE: Model-Based Controllers of Muscle-Actuated Characterscited by 13
- 2024DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systemscited by 12
- 2017Quantifying Performance of Bipedal Standing with Multi-channel EMGcited by 4
- 2023Fall Prediction for Bipedal Robots: The Standing Phasecited by 3
- 2023Characterization of Human Balance through a Reinforcement Learning-based Muscle Controllercited by 1
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Citing papers per year: 2026: 1.
1 paper.