Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired

TL;DR

This paper introduces MARVIN, a modular AI-powered robotic assistant prototype designed to aid the physically impaired with daily tasks, integrating neural networks for speech, vision, and language understanding.

Contribution

It presents the design and architecture of a novel assistive robot with modular components and neural network integration for improved independence of the physically impaired.

Findings

Prototype demonstrates effective speech recognition and object detection.

Modular design allows for flexible future enhancements.

Addresses key challenges in embodied AI for assistive applications.

Abstract

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.