  Balance assessment during physical rehabilitation often relies on
rubric-oriented battery tests to score a patient's physical capabilities,
leading to subjectivity. While some objective balance assessments exist, they
are often limited to tracking the center of pressure (COP), which does not
fully capture the whole-body postural stability. This study explores the use of
the center of mass (COM) state space and presents a promising avenue for
monitoring the balance capabilities in humans. We employ a musculoskeletal
model integrated with a balance controller, trained through reinforcement
learning (RL), to investigate balancing capabilities. The RL framework consists
of two interconnected neural networks governing balance recovery and muscle
coordination respectively, trained using Proximal Policy Optimization (PPO)
with reference state initialization, early termination, and multiple training
strategies. By exploring recovery from random initial COM states (position and
velocity) space for a trained controller, we obtain the final BR enclosing
successful balance recovery trajectories. Comparing the BRs with analytical
postural stability limits from a linear inverted pendulum model, we observe a
similar trend in successful COM states but more limited ranges in the
recoverable areas. We further investigate the effect of muscle weakness and
neural excitation delay on the BRs, revealing reduced balancing capability in
different regions. Overall, our approach of learning muscular balance
controllers presents a promising new method for establishing balance recovery
limits and objectively assessing balance capability in bipedal systems,
particularly in humans.

Machine Learning;Robotics;Systems and Control;Systems and Control