Viability, task switching, and fall avoidance of the simplest dynamic walker

Humans display great versatility when performing goal-directed tasks while walking. However, the extent to which such versatility helps with fall avoidance remains unclear. We recently demonstrated a functional connection between the motor regulation needed to achieve task goals (e.g. maintaining walking speed) and a simple walkers ability to reject large disturbances. Here, for the same model, we identify the viability kernel--the state space region in which the walker can step forever via at least one sequence of push-off inputs per state. We further find that only a few basins of attraction of the speed-regulated walkers steady-state gaits can fully cover the viability kernel. This highlights a potentially important role of task-level motor regulation in fall avoidance. Therefore, we posit an adaptive hierarchical control/regulation strategy that switches between different task-level regulators to avoid falls. Our hierarchical task switching controller only requires a target value of the regulated observable--a task switch--at each walking step, each chosen from a small, predetermined collection. Because humans have typically already learned to perform such tasks during nominal walking conditions, this suggests that the information cost of biologically implementing such controllers for the nervous system, including cognitive demands in humans, could be relatively low.