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We consider the task of object grasping with a prosthetic hand capable ofmultiple grasp types. In this setting, communicating the intended grasp typeoften requires a high user cognitive load which can be reduced adopting sharedautonomy frameworks. Among these, so-called eye-in-hand systems automaticallycontrol the hand pre-shaping before the grasp, based on visual input comingfrom a camera on the wrist. In this paper, we present an eye-in-handlearning-based approach for hand pre-shape classification from RGB sequences.Differently from previous work, we design the system to support the possibilityto grasp each considered object part with a different grasp type. In order toovercome the lack of data of this kind and reduce the need for tedious datacollection sessions for training the system, we devise a pipeline for renderingsynthetic visual sequences of hand trajectories. We develop a sensorized setupto acquire real human grasping sequences for benchmarking and show that,compared on practical use cases, models trained with our synthetic datasetachieve better generalization performance than models trained on real data. Wefinally integrate our model on the Hannes prosthetic hand and show itspractical effectiveness. We make publicly available the code and dataset toreproduce the presented results.

Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis