用人体手臂动作实时控制机器人，能生成训练外的新姿态。
From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation
- 用GRU-VAE学习机器人构型的潜在表示
- 实现实时映射，生成训练数据外的新轨迹
- 适合遥操作与灵巧机器人控制研究者
本文提出一种基于人体手臂动作的遥操作系统，用于控制冗余自由度机器人机械臂。我们采用基于GRU的变分自编码器（VAE）学习机械臂构型空间的潜在表示，捕捉其复杂的关节运动学特性。通过全连接神经网络将人体手臂构型映射至该潜在空间，利用VAE解码器实时生成对应的机械臂运动轨迹。实验表明，该方法在遥操作中表现良好，能够从训练未见的人体特征生成新的机械臂构型，拓展了操作的灵活性。
原文摘要 · Abstract (English)
This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real time through the VAE decoder. The proposed method shows promising results in teleoperating the manipulator, enabling the generation of novel manipulator configurations from human features that were not present during training.
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