Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.
翻译:优化和学习方法已被用于模仿和复制人类运动,然而,这些方法往往不健全,而且其概括性有限。这项工作提议为机器人操纵者建立一个等级控制结构,并提供在未知的互动动态中复制类似人类动作的能力。我们的结果表明,复制的终端效应或轨迹可以保留通过运动捕捉系统记录的人类最初运动的主要特征,并且能够抵御外部扰动。数据显示,由于硬件的物理限制无法达到人类运动所记录的速度,因此一些详细的运动很难复制。然而,这些技术问题可以通过使用更好的硬件来解决,而我们提议的算法仍然可以用来产生模拟动作。