Privacy-Preserving Active Learning for bio-inspired soft robotics maintenance during mission-critical recovery windows

This research explores combining privacy-preserving machine learning, specifically differential privacy and active learning, for the maintenance of bio-inspired soft robotics. The work addresses the challenge of retraining predictive maintenance models without exposing proprietary data during critical recovery windows.