Continuous Wrist Control on the Hannes Prosthesis: a Vision-based Shared Autonomy Framework

TL;DR

This paper introduces a vision-based shared autonomy system for continuous wrist control in prosthetic arms, enabling more natural grasping motions by integrating automatic and user inputs.

Contribution

It presents a novel computer vision framework for seamless, continuous wrist control in prosthetic devices, addressing a gap in dexterous prosthetic grasping techniques.

Findings

Effective wrist control following target objects

Improved naturalness of grasping motions

Successful deployment on Hannes prosthetic arm

Abstract

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.