Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs, commonly known as interlimb coordination. Several components underlying the interlimb coordination process (like distributed neural control circuits, local sensory feedback, and body-environment interactions during movement) have been recently identified and applied to the control systems of walking robots. However, while the sensory pathways of biological systems are plastic and can be continuously readjusted (referred to as sensory adaptation), those implemented on robots are typically static. They first need to be manually adjusted or optimized offline to obtain stable locomotion. In this study, we introduce a fast learning mechanism for online sensory adaptation. It can continuously adjust the strength of sensory pathways, thereby introducing flexible plasticity into the connections between sensory feedback and neural control circuits. We combine the sensory adaptation mechanism with distributed neural control circuits to acquire the adaptive and robust interlimb coordination of walking robots. This novel approach is also general and flexible. It can automatically adapt to different walking robots and allow them to perform stable self-organized locomotion as well as quickly deal with damage within a few walking steps. The adaptation of plasticity after damage or injury is considered here as lesion-induced plasticity. We validated our adaptive interlimb coordination approach with continuous online sensory adaptation on simulated 4-, 6-, 8-, and 20-legged robots. This study not only proposes an adaptive neural control system for artificial walking systems but also offers a possibility of invertebrate nervous systems with flexible plasticity for locomotion and adaptation to injury.

中文翻译：

---

自组织运动的一般分布式神经控制和感觉适应以及步行机器人对损伤的快速适应。

行走的动物，如无脊椎动物，可以有效地进行自组织和稳健的运动。他们还可以快速调整步态以应对受伤或损坏。如此复杂的成就主要是通过双腿之间的协调来完成的，俗称四肢协调。肢体间协调过程的几个组成部分（如分布式神经控制电路、局部感觉反馈和运动过程中的身体-环境相互作用）最近已被确定并应用于步行机器人的控制系统。然而，虽然生物系统的感觉通路是可塑性的并且可以不断地重新调整（称为感觉适应），但在机器人上实施的那些通路通常是静态的。它们首先需要手动调整或离线优化以获得稳定的运动。在这项研究中，我们引入了一种用于在线感官适应的快速学习机制。它可以不断调整感觉通路的强度，从而在感觉反馈和神经控制电路之间的连接中引入灵活的可塑性。我们将感觉适应机制与分布式神经控制电路相结合，以获得步行机器人的自适应和鲁棒的肢体间协调。这种新颖的方法也是通用且灵活的。它可以自动适应不同的行走机器人，让它们进行稳定的自组织运动，并在几步之内快速处理损坏。损伤或损伤后的可塑性适应在这里被认为是损伤引起的可塑性。我们通过对模拟 4-、6-、8 条腿和 20 条腿的机器人。这项研究不仅为人工步行系统提出了一种自适应神经控制系统，而且还为具有灵活可塑性的无脊椎动物神经系统提供了一种可能性，用于运动和适应伤害。