Introduction

SERMAS focus on developing methods and tools to help future XR systems be socially accepted.

Our take on XR systems is open ended and focusing on components to enable interaction with intelligent virtual or robotic agents

The project is funded by the European Union, learn more on the SERMAS website

Objectives

SERMAS Methodology

Systematically tackling the social acceptance of XR systems requires following a rigorous methodology of modelling, development, assessment, and in-lab and real-life validation.

The SERMAS Methodology helps XR engineers who, assisted by security analysts and social scientists, intend to develop next-generation XR systems that their human users can accept.

SERMAS Agent

The SERMAS Agent combines hardware, software, and algorithmic modules to implement an XR model and create personalised XR systems. It is also for non-specialised users.

Ppen natural language generation

The Agent accesses visual and language information and communicates with users using verbal and non-verbal signals. The SERMAS team will evaluate the model's ability to generate appropriate responses using automatic and human-based metrics.

Context awareness and integration of structured knowledge

The SERMAS Agent is context-aware, using its sensing suite to gather information on the environment and nearby users to determine appropriate actions and gestures.

Frictionless interaction

The SERMAS XR Agent interacts with users through human-to-human communication, utilising known effective communication mechanisms. The goal is to create a frictionless interaction between the user and the Agent.