Meta-Optimized Continual Adaptation for bio-inspired soft robotics maintenance with zero-trust governance guarantees

The author encountered significant degradation in a bio-inspired soft robotic gripper, revealing the inadequacy of standard reinforcement learning for time-evolving simulation-to-reality gaps. This led to a focus on meta-optimized continual adaptation for maintenance, integrating zero-trust governance.