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

Journal:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Published:
DOI:
10.1109/iros60139.2025.11247244
Affiliations:
1
Authors:
5
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Institutions Authors Share
State Key Laboratory of Fluid Power and Mechatronic Systems, ZJU, China
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