Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired
This addresses the problem of independence for the physically impaired, but it is incremental as it applies existing AI methods to a new domain.
The paper tackles the lack of embodied AI applications for assistive technologies by presenting MARVIN, a modular robotic assistant prototype designed to help the physically impaired perform daily tasks, utilizing state-of-the-art neural networks for functions like speech recognition and object detection.
The physically impaired commonly have difficulties performing simple routine tasks without relying on other individuals who are not always readily available and thus make them strive for independence. While their impaired abilities can in many cases be augmented (to certain degrees) with the use of assistive technologies, there has been little attention to their applications in embodied AI with assistive technologies. This paper presents the modular framework, architecture, and design of the mid-fidelity prototype of MARVIN: an artificial-intelligence-powered robotic assistant designed to help the physically impaired in performing simple day-to-day tasks. The prototype features a trivial locomotion unit and also utilizes various state-of-the-art neural network architectures for specific modular components of the system. These components perform specialized functions, such as automatic speech recognition, object detection, natural language understanding, speech synthesis, etc. We also discuss the constraints, challenges encountered, potential future applications and improvements towards succeeding prototypes.