Learning of Dexterous Object Manipulation in a Virtual Reality Environment

Humans have unrivalled abilities to perform dexterous object manipulation. This requires the sensorimotor system to quickly adapt to environmental changes and predictively counter act the external disturbances. Many studies have focused on the anticipatory control of digits with real-world experiments. However, examining manipulation using virtual reality with haptic devices expands the possibilities of investigation. In this work, participants grasped and lifted an inverted T-shaped object in a virtual reality setup. The graspable surface of the object was either constrained to a small area or unconstrained. The position of the object's center of mass changed between blocks, and the participants were asked to minimize the rotation of the object during the lift. Our results show that, consistent with the results of real-world experiments, participants gradually learn to adjust the digit positions and forces to predictively compensate for the torque due to the shifted center of mass prior to liftoff. The only major difference found was that the length of trials needed during the adaptation phase to each condition increased from 3 in real-world to 5 in virtual environment.