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 walker’s 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 walker’s 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.

中文翻译：

---

最简单的动态步行者的生存能力、任务切换和跌倒避免

在行走时执行目标导向的任务时，人类表现出极大的多功能性。然而，这种多功能性在多大程度上有助于避免跌倒仍不清楚。我们最近展示了实现任务目标（例如保持步行速度）所需的运动调节与简单步行者拒绝大干扰的能力之间的功能联系。在这里，对于相同的模型，我们确定了可行性内核——步行者可以通过每个状态至少一个推离输入序列永远在其中行走的状态空间区域。我们进一步发现，速度调节的步行者的稳态步态只有少数几个吸引盆地可以完全覆盖活力内核。这突出了任务级运动调节在避免跌倒中的潜在重要作用。所以，我们提出了一种自适应分层控制/调节策略，可以在不同的任务级调节器之间切换以避免跌倒。我们的分层任务切换控制器只需要一个受监管的可观察对象的目标值——一个“任务切换”——在每个步行步骤中，每个步骤都从一个小的预定集合中选择。因为人类通常已经学会在标称步行条件下执行此类任务，这表明以生物学方式为神经系统实施此类控制器的“信息成本”（包括人类的认知需求）可能相对较低。