从人手到机器人肢体：面向遥操作的运动技能具身化研究
From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation
AI总结：
本文提出一种基于GRU变分自编码器和全连接网络的遥操作系统，将人类手臂手势映射到冗余自由度机械臂的潜在构型空间，实现实时轨迹生成并能泛化到训练中未见过的构型。
摘要：
本文提出了一种利用人类手臂手势控制冗余自由度机器人机械臂的遥操作系统。我们提出了一种基于GRU的变分自编码器，用于学习机械臂构型空间的潜在表示，以捕捉其复杂的关节运动学特性。一个全连接神经网络将人类手臂构型映射到该潜在空间中，使系统能够通过VAE解码器实时模仿并生成相应的机械臂轨迹。所提出的方法在机械臂遥操作中展现了有前景的结果，能够从训练中未出现过的人类特征生成新颖的机械臂构型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
