Control of overactuated, high-dimensional musculoskeletal models with delayed nonlinear dynamics

Develop control algorithms for physiologically realistic, muscle-actuated musculoskeletal models that are overactuated, high-dimensional, and exhibit delayed nonlinear activation dynamics.

Background

The paper highlights that musculoskeletal (MSK) systems pose unique control difficulties due to overactuation, high dimensionality, and delayed nonlinear muscle activation dynamics. Prior approaches using high-level, sparse objectives often produce unnatural behaviors such as peculiar gaits or unrealistic postures, underscoring the need for more effective controllers for realistic MSK systems.

MuscleMimic addresses training efficiency via GPU-accelerated simulation and imitation learning, but the broader challenge of robustly controlling physiologically realistic MSK models remains unresolved and is explicitly stated as open.