Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909519976595456 |
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| author | Stracquadanio, Giuseppe Vasile, Federico Maiettini, Elisa Boccardo, Nicolò Natale, Lorenzo |
| author_facet | Stracquadanio, Giuseppe Vasile, Federico Maiettini, Elisa Boccardo, Nicolò Natale, Lorenzo |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_00466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping Stracquadanio, Giuseppe Vasile, Federico Maiettini, Elisa Boccardo, Nicolò Natale, Lorenzo Robotics Computer Vision and Pattern Recognition Systems and Control 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/. |
| title | Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2503.00466 |