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arXiv · 2503.00466

Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping

Giuseppe Stracquadanio·Federico Vasile·Elisa Maiettini·Nicolò Boccardo·Lorenzo Natale
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Abstract

One of the most important research challenges in upper-limb prosthetics is enhancing the user-prosthesis communication to closely resemble the experience of a natural limb. As prosthetic devices become more complex, users often struggle to control the additional degrees of freedom. In this context, leveraging shared-autonomy principles can significantly improve the usability of these systems. In this paper, we present a novel eye-in-hand prosthetic grasping system that follows these principles. Our system initiates the approach-to-grasp action based on user's command and automatically configures the DoFs of a prosthetic hand. First, it reconstructs the 3D geometry of the target object without the need of a depth camera. Then, it tracks the hand motion during the approach-to-grasp action and finally selects a candidate grasp configuration according to user's intentions. We deploy our system on the Hannes prosthetic hand and test it on able-bodied subjects and amputees to validate its effectiveness. We compare it with a multi-DoF prosthetic control baseline and find that our method enables faster grasps, while simplifying the user experience. Code and demo videos are available online at https://hsp-iit.github.io/byogg/.

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Giuseppe Stracquadanio, Federico Vasile, Elisa Maiettini, Nicolò Boccardo, Lorenzo Natale. 2025-03-01. Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping. https://arxiv.org/abs/2503.00466

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