通过触觉反馈提升机器人抓取力控制，帮助残障用户更精准操作
TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback
- 融合肌电与视觉触觉信号，实现力感知到力反馈的闭环控制
- 用户实验表明抓取稳定性与任务成功率显著提升
- 适合上肢残疾者使用，尤其依赖间接反馈的辅助机器人场景
近年来的遥操作技术已能精确控制灵巧机械手的指节位置以实现目标抓取形态。然而，精确的位置控制常忽视对抓取力度的调节，而这一能力对于处理不同硬度、纹理和形状的物体至关重要。这对缺乏自然触觉反馈的上肢残疾用户尤为挑战，他们需依赖间接线索判断合适力度。为此，我们提出触觉增强抓取助手（TEGA），一个闭环辅助遥操作框架，融合肌电驱动的意图转力推断与视觉-触觉传感，通过可穿戴振动触觉背心实时反馈力信息，实现抓取过程中的直观、按比例力调节。用户研究表明，该系统显著提升了抓取稳定性和任务成功率，展现出在辅助机器人应用中的巨大潜力。
原文摘要 · Abstract (English)
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.
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