Zilin Xing

Adversarial Augmentation for Task-Parameterized Underwater Skill Learning via Digital Twins*

Learning from Demonstration (LfD) provides an efficient approach to acquiring diverse underwater skills, with task-parameterized learning enhancing the generalization of policies. However, collecting comprehensive underwater demonstrations across various conditions remains a significant challenge. I