Privacy-Preserving Active Learning for bio-inspired soft robotics maintenance under real-time policy constraints Introduction: A Discovery in the Lab It started with a failure—a soft robotic octopus arm that had developed micro-cracks after 10,000 cycles of underwater manipulation. While exploring ways to extend its operational lifetime, I discovered something profound: the very data we needed to predict maintenance events was also exposing proprietary design parameters and operational patterns. This tension between data utility and privacy became the central focus of my research journey. In my experimentation with bio-inspired soft robotics—those fascinating actuators made from elastomers, hydrogels, and shape-memory polymers that mimic biological organisms—I realized that traditional maintenance approaches were fundamentally flawed. They either required massive labeled datasets (which are expensive and privacy-invasive) or relied on fixed schedules that ignored real-time degradation patterns.…