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

arXiv: https://arxiv.org/abs/2506.09383 · HTML: https://mlsanity.com/paper/2506.09383/

## Key facts

- Cited by 1 papers 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 (most cited first, 12 of 12)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo](https://mlsanity.com/paper/2212.00541/) (2022)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation](https://mlsanity.com/paper/2307.03716/) (2023)
- [Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation](https://mlsanity.com/paper/2312.05473/) (2023)
- [MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters](https://mlsanity.com/paper/2312.07340/) (2023)
- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [Quantifying Performance of Bipedal Standing with Multi-channel EMG](https://mlsanity.com/paper/1711.07894/) (2017)
- [Fall Prediction for Bipedal Robots: The Standing Phase](https://mlsanity.com/paper/2309.14546/) (2023)
- [Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller](https://mlsanity.com/paper/2308.04462/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation](https://mlsanity.com/paper/2603.04166/) (2026)

## 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.