Finding an efficient way to adapt robot trajectory is a priority to improve
overall performance of robots. One approach for trajectory planning is through
transferring human-like skills to robots by Learning from Demonstrations (LfD).
The human demonstration is considered the target motion to mimic. However,
human motion is typically optimal for human embodiment but not for robots
because of the differences between human biomechanics and robot dynamics. The
Dynamic Movement Primitives (DMP) framework is a viable solution for this
limitation of LfD, but it requires tuning the second-order dynamics in the
formulation. Our contribution is introducing a systematic method to extract the
dynamic features from human demonstration to auto-tune the parameters in the
DMP framework. In addition to its use with LfD, another utility of the proposed
method is that it can readily be used in conjunction with Reinforcement
Learning (RL) for robot training. In this way, the extracted features
facilitate the transfer of human skills by allowing the robot to explore the
possible trajectories more efficiently and increasing robot compliance
significantly. We introduced a methodology to extract the dynamic features from
multiple trajectories based on the optimization of human-likeness and
similarity in the parametric space. Our method was implemented into an actual
human-robot setup to extract human dynamic features and used to regenerate the
robot trajectories following both LfD and RL with DMP. It resulted in a stable
performance of the robot, maintaining a high degree of human-likeness based on
accumulated distance error as good as the best heuristic tuning.

提出了一种系统的方法来从人类演示中提取动态特征，以自动调整 DMP 框架中的参数，此方法可与 LfD 和 RL 一同使用，可使机器人更有效地探索可能的轨迹，从而显着提高机器人的遵从性，并在实际的人机交互实验中得以验证。