A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons

TL;DR

This paper presents a multi-layer sim-to-real framework using VR data to develop personalized gaze-driven control models for assistive neck exoskeletons, demonstrating effective transfer from simulation to real-world application.

Contribution

It introduces a novel multi-layer controller selection framework and gaze-driven models trained in VR for personalized assistive neck exoskeleton control.

Findings

Two novel gaze-driven models achieved strong real-world performance.

Controller effectiveness varies across individuals, emphasizing personalization.

VR-based evaluation accelerates development of assistive robotic control.

Abstract

Dropped head syndrome, caused by neck muscle weakness from neurological diseases, severely impairs an individual's ability to support and move their head, causing pain and making everyday tasks challenging. Our long-term goal is to develop an assistive powered neck exoskeleton that restores natural movement. However, predicting a user's intended head movement remains a key challenge. We leverage virtual reality (VR) to collect coupled eye and head movement data from healthy individuals to train models capable of predicting head movement based solely on eye gaze. We also propose a novel multi-layer controller selection framework, where head control strategies are evaluated across decreasing levels of abstraction -- from simulation and VR to a physical neck exoskeleton. This pipeline effectively rejects poor-performing controllers early, identifying two novel gaze-driven models that achieve strong performance when deployed on the physical exoskeleton. Our results reveal that no single controller is universally preferred, highlighting the necessity for personalization in gaze-driven assistive control. Our work demonstrates the utility of VR-based evaluation for accelerating the development of intuitive, safe, and personalized assistive robots.