Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.

利用基于视觉的软式机器人手指通过学习六维受力和力矩（FT）的监督式变分自编码器（SVAE），该研究调查了从陆地到水下的抓握知识的可转移性。结果表明，训练的SVAE模型学习了一系列的潜在表示，可以从陆地到水中进行传递，提供了针对商用FT传感器环境变化的优越适应性。通过触觉智能实现的软、精细和反应灵敏的抓握提高了夹持器在水下的交互可靠性和稳健性，极大地降低了成本，为基于学习的智能抓握支持环境和海洋研究的基础科学发现铺平了道路。

对陆地到水下的抓握学习进行自编码