視線駆動型支援首装具のための多層シミュレーションから実機へのフレームワーク
A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons
VRで視線と頭部運動のデータを収集し、視線のみから頭部運動を予測するモデルを訓練。シミュレーションから実機まで段階的に評価する多層制御器選択フレームワークを提案し、物理的な首装具で高性能な視線駆動モデルを特定した。
詳しい要約
1. どんなもの?
2. 先行研究と比べてどこがすごい?
3. 技術・手法の肝は?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Colin Rubow, Eric Brewer, Ian Bales, Haohan Zhang, Daniel S. Brown
分類: cs.RO
原文アブストラクト
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.
