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-inhand systems automatically control the hand aperture and preshaping before the grasp, based on visual input coming from a camera on the wrist. In this work, we present an eye-inhand learning-based approach for hand pre-shape classification from RGB sequences. In order to reduce the need for tedious data collection sessions for training the system, we devise a pipeline for rendering synthetic visual sequences of hand trajectories for the purpose. We tackle the peculiarity of the eye-in-hand setting by means of a model for the human arm trajectories, with domain randomization over relevant visual elements. 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. Our code, real and synthetic datasets will be released upon acceptance.
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-inhand 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 learningbased 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 † .
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