TEGA: センサ融合と閉ループ触覚フィードバックによる支援ロボティクス向け触覚強化把持アシスタント
TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback
筋電位からの力意図推定と視触覚センシングを融合し、ウェアラブル触覚ベストでリアルタイムに振動フィードバックを返す閉ループ遠隔操作フレームワークを提案し、把持力の直感的調整を可能にした。ユーザー実験で把持安定性とタスク成功率の向上を実証した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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6. 次に読むべき論文は？
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著者: Hengxu You, Tianyu Zhou, Fang Xu, Kaleb Smith, Eric Jing Du
分類: cs.RO
原文アブストラクト
Recent advances in teleoperation have enabled sophisticated manipulation of dexterous robotic hands, with most systems concentrating on guiding finger positions to achieve desired grasp configurations. However, while accurate finger positioning is essential, it often overlooks the equally critical task of grasp force modulation, vital for handling objects of diverse hardness, texture, and shape. This limitation poses a significant challenge for users, especially individuals with upper limb disabilities who lack natural tactile feedback and rely on indirect cues to infer appropriate force levels. To address this gap, We present the tactile enhanced grasping assistant (TEGA), a closed loop assistive teleoperation framework that fuses EMG based intent2force inference with visuotactile sensing mapped into real time vibrotactile feedback via a wearable haptic vest, enabling intuitive, proportional force adjustment during manipulation. A wearable haptic vest delivers real time tactile feedback, allowing users to dynamically refine grasp force during manipulation. User studies confirm that the system substantially improves grasp stability and task success, underscoring its potential for assistive robotic applications.
