  We consider the task of object grasping with a prosthetic hand capable of
multiple grasp types. In this setting, communicating the intended grasp type
often requires a high user cognitive load which can be reduced adopting shared
autonomy frameworks. Among these, so-called eye-in-hand systems automatically
control the hand pre-shaping before the grasp, based on visual input coming
from a camera on the wrist. In this paper, we present an eye-in-hand
learning-based approach for hand pre-shape classification from RGB sequences.
Differently from previous work, we design the system to support the possibility
to grasp each considered object part with a different grasp type. In order to
overcome the lack of data of this kind and reduce the need for tedious data
collection sessions for training the system, we devise a pipeline for rendering
synthetic visual sequences of hand trajectories. We develop a sensorized setup
to acquire real human grasping sequences for benchmarking and show that,
compared on practical use cases, models trained with our synthetic dataset
achieve better generalization performance than models trained on real data. We
finally integrate our model on the Hannes prosthetic hand and show its
practical effectiveness. We make publicly available the code and dataset to
reproduce the presented results.
