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

2025-06-11

AI Summary

This study addresses the lack of quantitative models for human and bipedal robot static balance and fall mechanisms. We propose a hierarchical optimal control simulation framework based on a full-body musculoskeletal model. Methodologically, we achieve, for the first time, muscle-level dynamic simulation of the entire process—static balance, loss of balance, and falling—integrating clinical motion data-driven validation with hip-exoskeleton torque coupling modeling. Key contributions include: (1) characterization of spatiotemporal dynamics and muscle synergies underlying stable upright stance; (2) quantification of balance degradation due to simulated muscle impairment; and (3) development of an exoskeleton assistance strategy that reduces peak activation of key muscles by 32%, extends time-to-loss-of-balance under perturbation by 41%, and successfully reproduces clinically observed fall contact patterns. These results establish a new paradigm for investigating neuromuscular control principles and designing rehabilitation exoskeletons.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Understand static balance and falling dynamics in humans
• Simulate human balance using musculoskeletal system
• Improve balance maintenance with hip exoskeleton assistance

Innovation

• Hierarchical control pipeline for balance
• Whole-body musculoskeletal system simulation
• Hip exoskeleton assistance reduces effort