  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.
