Jiang Xisheng, Wu Baolei, Li Simin, Zhu Yongtong, Liang Guoxiang, Yuan Ye, Li Qingdu, Zhang Jianwei
School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China.
Biomimetics (Basel). 2025 Mar 19;10(3):190. doi: 10.3390/biomimetics10030190.
Human-robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitation remains a challenging task. Traditional retargeting algorithms, while effective in mapping human motion to robots, typically either ensure similarity in arm configuration (joint space-based) or focus solely on tracking the end-effector position (Cartesian space-based). This creates a conflict between the liveliness and accuracy of robot motion. To address this issue, this paper proposes an improved retargeting algorithm that ensures both the similarity of the robot's arm configuration to that of the human body and accurate end-effector position tracking. Additionally, a multi-person pose estimation algorithm is introduced, enabling real-time capture of multiple imitators' movements using a single RGB-D camera. The captured motion data are used as input to the improved retargeting algorithm, enabling multi-robot collaboration tasks. Experimental results demonstrate that the proposed algorithm effectively ensures consistency in arm configuration and precise end-effector position tracking. Furthermore, the collaborative experiments validate the generalizability of the improved retargeting algorithm and the superior real-time performance of the multi-person pose estimation algorithm.
人机交互（HRI）是仿人机器人领域的一项关键技术，而动作模仿是实现高效人机交互最直接的方式之一。然而，由于人体与机器人在结构、运动范围和关节扭矩方面存在显著差异，动作模仿仍然是一项具有挑战性的任务。传统的重定向算法虽然能有效地将人类动作映射到机器人上，但通常要么确保手臂配置的相似性（基于关节空间），要么只专注于跟踪末端执行器的位置（基于笛卡尔空间）。这就导致了机器人动作的生动性和准确性之间的冲突。为了解决这个问题，本文提出了一种改进的重定向算法，该算法既能确保机器人手臂配置与人体的相似性，又能精确跟踪末端执行器的位置。此外，还引入了一种多人姿态估计算法，能够使用单个RGB-D相机实时捕捉多个模仿者的动作。捕捉到的运动数据被用作改进的重定向算法的输入，从而实现多机器人协作任务。实验结果表明，所提出的算法有效地确保了手臂配置的一致性和末端执行器位置的精确跟踪。此外，协作实验验证了改进的重定向算法的通用性以及多人姿态估计算法卓越的实时性能。
