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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\u2010land to underwater via a vision\u2010based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high\u2010framerate camera captures the whole\u2010body deformations while a soft robotic finger interacts with physical objects on\u2010land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. 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