TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics Via Sensor Fusion and Closed-Loop Haptic Feedback
Hengxu You, Tianyu Zhou, Fang Xu, Kaleb Smith, Jing Du
Abstract
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 propose a novel teleoperation framework that integrates EMG-based force control, AR-based pose tracking, and visuo-tactile sensing to enable precise and intuitive force adjustment. A wearable haptic vest delivers real-time tactile feedback, allowing users to dynamically refine grasp force during manipulation. User studies confirm that our dual-loop control system substantially improves grasp stability and task success, underscoring its potential for assistive robotic applications.