用视觉共享控制让假肢手腕自然跟随目标，减少用户代偿动作。
Continuous Wrist Control on the Hannes Prosthesis: a Vision-based Shared Autonomy Framework
- 基于视觉的共治框架，融合用户意图与自动控制
- 实现假肢手腕连续运动，精准对准抓取目标
- 适用于希望提升假肢自然度的用户和研究者
多数假肢抓握控制方法聚焦于灵巧手指，忽视手腕运动，导致用户需通过肘、肩、髋部代偿来调整手腕姿势。本文提出一种基于计算机视觉的共享自治系统，实现假肢手臂手腕自由度的连续控制，促进更自然的接近-抓取动作。该流程可无缝引导假肢手腕追踪目标物体，并根据用户意图最终定向抓取。我们通过定量分析验证各组件有效性，并在Hannes假肢上部署该方法。代码与视频见：https://hsp-iit.github.io/hannes-wrist-control。
原文摘要 · Abstract (English)
Most control techniques for prosthetic grasping focus on dexterous fingers control, but overlook the wrist motion. This forces the user to perform compensatory movements with the elbow, shoulder and hip to adapt the wrist for grasping. We propose a computer vision-based system that leverages the collaboration between the user and an automatic system in a shared autonomy framework, to perform continuous control of the wrist degrees of freedom in a prosthetic arm, promoting a more natural approach-to-grasp motion. Our pipeline allows to seamlessly control the prosthetic wrist to follow the target object and finally orient it for grasping according to the user intent. We assess the effectiveness of each system component through quantitative analysis and finally deploy our method on the Hannes prosthetic arm. Code and videos: https://hsp-iit.github.io/hannes-wrist-control.
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