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.

References

Moreover, controlling physiologically realistic MSK models that are overactuated, high-dimensional, and exhibit delayed nonlinear dynamics remains an open challenge.

— Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale  (2603.25544 - Li et al., 26 Mar 2026) in Introduction (Section 1)

We do observe slightly less knee flexion, more frontal-plane motion, and more excessive arm swing than that exhibited by elite sprinters. Identifying the source of these differences--e.g., deficiencies in the musculoskeletal model vs. optimization failure remains future work.

— Learning Realistic Athletic Sprinting Without Demonstrations  (2609.11083 - Wang et al., 10 Sep 2026) in Section 5.1, subsection “Visual Realism”

We found that our policy optimization was unable to find a good solution when using only a sparse reward, in which the agent receives reward only at the end of the episode, equal to the maximum height reached during that episode. With a high dimensional action space, this becomes a challenging problem to optimize over, and likely requires more effective exploration.

— Learning Realistic Athletic Sprinting Without Demonstrations  (2609.11083 - Wang et al., 10 Sep 2026) in Appendix, Section “Additional Task: Standing Vertical Jump”