Insect‐Inspired Resilient Machines

Abstract

Mechanical resilience is crucial for both animals and machines. Repairing or replacing damaged components of machines is often costly and time‐consuming. Many walking insects, especially species that autotomize legs as a predator‐avoidance strategy, exhibit remarkable adaptive control of their leg movement dynamics to compensate for leg loss. The embodied adaptation of leg control in insects can be informative for robotics to develop control strategies for damage compensation. From this point, the study utilizes the stick insect Medauroidea extradentata as a model organism to investigate the effects of leg amputation on the compensatory control of walking behavior. A decentralized adaptive resilient neural control system is proposed, leveraging self‐embodied resilience strategies, for legged robots. Unlike model‐based or machine learning‐based approaches, relying on accurate mathematical models or extensive training data, the neural control system achieves self‐organized gait patterns and adaptive leg movements through minimal sensory feedback, coupled with neural dynamics, synaptic plasticity, and robot‐environment interactions. This embodied neural control approach is validated and demonstrated on simulated and real insect robots, resulting in robust locomotion and rapid adaptation (within seconds) to various leg loss cases. The combined findings reveal the potential for insect‐inspired embodied emergent resilience in complex robotic systems toward resilient robotics.