用机器人抓取生成技术提升假肢抓握速度与易用性
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
- 基于用户指令自动配置假肢手的自由度，实现眼手协同抓握
- 无需深度相机即可重建目标物体3D几何，提升系统实用性
- 在截肢者和健壮受试者上验证，显著加快抓握速度并简化操作
上肢假肢研究的核心挑战之一是增强人机交互，使其更接近自然肢体体验。随着假肢功能复杂化，用户往往难以操控额外自由度。本文提出一种新型眼手协同假肢抓握系统，遵循共主控原则：系统根据用户指令启动抓握动作，并自动配置假肢手的自由度。首先，在无深度相机条件下重建目标物体的3D几何；其次，跟踪手部运动过程；最后依据用户意图选择最优抓握姿态。我们在Hannes假肢手上部署该系统，并在健壮受试者和截肢者中进行测试。与多自由度控制基线相比，本方法显著提升了抓握速度，同时简化了用户操作。代码与演示视频见https://hsp-iit.github.io/byogg/。
原文摘要 · Abstract (English)
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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