Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments

TL;DR

Marvin is a novel omni-directional robotic platform designed for domestic elderly assistance, integrating agile mobility, offline AI perception, and modular design to enhance safety, connectivity, and privacy in home environments.

Contribution

The paper introduces Marvin, a lightweight, omni-directional robot with offline AI capabilities, combining mobility, perception, and interaction for domestic elderly care.

Findings

Effective obstacle avoidance in cluttered spaces

Offline perception and vocal control on embedded hardware

Enhanced privacy through local data processing

Abstract

Population ageing and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare, and elderly monitoring is one of the services a robot can handle for collective well-being. Despite these emerging needs, in the actual landscape of robotic assistants there are no platform which successfully combines a reliable mobility in cluttered domestic spaces, with lightweight and offline Artificial Intelligence (AI) solutions for perception and interaction. In this work, we present Marvin, a novel assistive robotic platform we developed with a modular layer-based architecture, merging a flexible mechanical design with cutting-edge AI for perception and vocal control. We focus the design of Marvin on three target service functions: monitoring of elderly and reduced-mobility subjects, remote presence and connectivity, and night assistance. Compared to previous works, we propose a tiny omnidirectional platform, which enables agile mobility and effective obstacle avoidance. Moreover, we design a controllable positioning device, which easily allows the user to access the interface for connectivity and extends the visual range of the camera sensor. Nonetheless, we delicately consider the privacy issues arising from private data collection on cloud services, a critical aspect of commercial AI-based assistants. To this end, we demonstrate how lightweight deep learning solutions for visual perception and vocal command can be adopted, completely running offline on the embedded hardware of the robot.