Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis
This work addresses the challenge of intuitive grasp selection for prosthetic hand users, representing an incremental improvement in shared autonomy frameworks.
The paper tackles the problem of reducing cognitive load in prosthetic hand grasping by developing an eye-in-hand system that classifies hand pre-shapes from RGB sequences using synthetic training data, achieving better generalization than models trained on real data.
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.