Continuous Wrist Control on the Hannes Prosthesis: a Vision-based Shared Autonomy Framework
This work addresses a specific issue for prosthetic arm users by reducing compensatory movements, though it is incremental as it builds on existing shared autonomy and vision methods.
The paper tackles the problem of prosthetic wrist control, which is often overlooked in favor of finger dexterity, by proposing a vision-based shared autonomy framework that enables continuous wrist motion for more natural grasping, and demonstrates its deployment on the Hannes prosthetic arm with quantitative analysis.
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.