Enhancing Independence through Intelligent Robotics: An AI-Driven Assistive Robotics Interface

Applying methods in artificial intelligence to the field of assistive robotics has the potential to increase the independence of people with disabilities. The usage of AI to realize a shared control in this context is controversial, due to the high complexity of everyday tasks and the needed safety requirements. This paper presents the development of a user interface for AI-driven assistive robotic arms (ARA) that aims to assist people with physical disabilities in performing daily activities. This interface allows the user to select object manipulation tasks based on the objects recognized in a live video stream. Further, we compare several state-of-the-art, real-time object detection models to facilitate automatic robotic control. The results demonstrate the feasibility of the model and its potential integration into the overall robotic system.

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Publication Details

Journal
OJS
Published
2025-03-24
DOI
https://doi.org/10.60643/urai.v2023p50
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhancing Independence through Intelligent Robotics: An AI-Driven Assistive Robotics Interface

OJS
Robot Manipulation and Learning
article

Enhancing Independence through Intelligent Robotics: An AI-Driven Assistive Robotics Interface

article en

Abstract

Applying methods in artificial intelligence to the field of assistive robotics has the potential to increase the independence of people with disabilities. The usage of AI to realize a shared control in this context is controversial, due to the high complexity of everyday tasks and the needed safety requirements. This paper presents the development of a user interface for AI-driven assistive robotic arms (ARA) that aims to assist people with physical disabilities in performing daily activities. This interface allows the user to select object manipulation tasks based on the objects recognized in a live video stream. Further, we compare several state-of-the-art, real-time object detection models to facilitate automatic robotic control. The results demonstrate the feasibility of the model and its potential integration into the overall robotic system.

OJS
Offenburg University of Applied Sciences (DE), Software (Spain) (ES)
Reduced inequalities
Openalex Percentile: Top 46%
Robot Manipulation and Learning
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