用肩部肌力传感实现手臂姿态实时估计，助力机器人避障
Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device for Human-Robot Interaction
- 用变压器模型将肩部肌力信号映射为手臂姿态
- 在真实场景中实现无视觉依赖的机器人避碰
- 模型可跨用户迁移，精度下降小
精准的人体姿态估计对有效人机交互至关重要。通过观察用户手臂动作，机器人可作出适当响应，如提供协助或避免碰撞。虽然视觉感知具备潜力，但易受光照不良或遮挡影响。可穿戴惯性传感器虽有用，却需频繁校准，且无法提供绝对位置信息。肌力肌电（FMG）是一种替代方法，通过外部测量肌肉扰动实现动作捕捉，已有研究用于手指运动，但尚未应用于全臂状态估计。本文提出一种可穿戴肩部FMG设备，结合基于变压器的模型，实时将肩部肌力信号映射为人体手臂物理姿态。该模型具备跨用户迁移能力，仅轻微精度下降。通过与机械臂在真实环境中的实验，验证了无需视觉感知即可实现碰撞规避。
原文摘要 · Abstract (English)
Accurate human pose estimation is essential for effective Human-Robot Interaction (HRI). By observing a user's arm movements, robots can respond appropriately, whether it's providing assistance or avoiding collisions. While visual perception offers potential for human pose estimation, it can be hindered by factors like poor lighting or occlusions. Additionally, wearable inertial sensors, though useful, require frequent calibration as they do not provide absolute position information. Force-myography (FMG) is an alternative approach where muscle perturbations are externally measured. It has been used to observe finger movements, but its application to full arm state estimation is unexplored. In this letter, we investigate the use of a wearable FMG device that can observe the state of the human arm for real-time applications of HRI. We propose a Transformer-based model to map FMG measurements from the shoulder of the user to the physical pose of the arm. The model is also shown to be transferable to other users with limited decline in accuracy. Through real-world experiments with a robotic arm, we demonstrate collision avoidance without relying on visual perception.
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