Soft Vision-Based Tactile-Enabled SixthFinger: Advancing Daily Objects Manipulation for Stroke Survivors

TL;DR

This paper presents a soft, vision-based tactile-enabled robotic finger designed to assist stroke survivors with object manipulation by autonomously adjusting grip based on tactile feedback, demonstrating robustness in real-world tasks.

Contribution

Introduces a novel soft robotic finger with vision-based tactile sensing and a transformer framework for improved object manipulation in stroke rehabilitation.

Findings

Successfully manipulated various everyday objects

System adjusted grip force autonomously in real-time

Demonstrated robustness in real-world scenarios

Abstract

The presence of post-stroke grasping deficiencies highlights the critical need for the development and implementation of advanced compensatory strategies. This paper introduces a novel system to aid chronic stroke survivors through the development of a soft, vision-based, tactile-enabled extra robotic finger. By incorporating vision-based tactile sensing, the system autonomously adjusts grip force in response to slippage detection. This synergy not only ensures mechanical stability but also enriches tactile feedback, mimicking the dynamics of human-object interactions. At the core of our approach is a transformer-based framework trained on a comprehensive tactile dataset encompassing objects with a wide range of morphological properties, including variations in shape, size, weight, texture, and hardness. Furthermore, we validated the system's robustness in real-world applications, where it successfully manipulated various everyday objects. The promising results highlight the potential of this approach to improve the quality of life for stroke survivors.