Balance Control

Balance control research focuses on understanding and improving the ability of biological and robotic systems to maintain stability, primarily through the analysis of postural sway and the development of effective control strategies. Current research employs diverse approaches, including reinforcement learning algorithms (like Proximal Policy Optimization), model predictive control frameworks incorporating capture point tracking, and the use of musculoskeletal models to simulate and analyze human and robotic balance. These advancements have implications for improving safety in physically demanding occupations (e.g., construction), enhancing the capabilities of assistive robots for elderly or impaired individuals, and furthering our understanding of human motor control and neurological function.