Guided by direct experiments on many-legged animals, mathematical models and physical models (robots), we postulate a hierarchical family of control loops that necessarily include constraints of the body's mechanics. At the lowest end of this neuromechanical hierarchy, we hypothesize the primacy of mechanical feedback - neural clocks exciting tuned muscles acting through chosen skeletal postures. Control algorithms appear embedded in the form and skeleton of the animal itself. The control potential of muscles must be realized through complex, viscoelastic bodies. Bodies can absorb and redirect energy for transitions. Tails can be used as inertial control devices. On top of this physical layer reside sensory feedback driven reflexes that increase an animal's stability further and, at the highest level, environmental sensing that operates on a stride-to-stride timescale to direct the animal's body. Most importantly, locomotion requires an effective interaction with the environment. Understanding control requires understanding the coupling to environment. Amazing feet permit creatures such as geckos to climb up walls at over meter per second without using claws, glue or suction - just molecular forces using hairy toes. Fundamental principles of animal locomotion have inspired the design of self-clearing dry adhesives and autonomous legged robots such as the Ariel, Mecho-gecko, Sprawl, RHex, RiSE and Stickybot that can aid in search and rescue, inspection, detection and exploration.
Template-anchor modeling - is necessary look at K_leg = F/mg/dL/L in the SLIP model as a dimensionless param.
During dynamical activity the forces are filtered through the entire musculoskeletal system and the control effort is difficult to predict without taking into consideration the dynamics of the system.