Explainable Causal Reinforcement Learning for bio-inspired soft robotics maintenance in carbon-negative infrastructure Introduction: A Personal Journey into the Intersection of Robotics and Sustainability It was a rainy Tuesday afternoon in March when I first stumbled upon a paper that would fundamentally reshape my understanding of how AI could bridge the gap between biological inspiration and sustainable infrastructure. I was deep into my research on reinforcement learning for soft robotics, trying to figure out how to make these squishy, biomimetic machines maintain themselves in harsh environments. The challenge was immense—soft robots, inspired by octopus arms and elephant trunks, are notoriously difficult to model and control. But what if we could make them learn to repair themselves in carbon-negative infrastructure, where every gram of material and joule of energy matters? As I was experimenting with traditional reinforcement learning approaches, I kept hitting a wall: the "black box" problem.…