  The existing Motion Imitation models typically require expert data obtained
through MoCap devices, but the vast amount of training data needed is difficult
to acquire, necessitating substantial investments of financial resources,
manpower, and time. This project combines 3D human pose estimation with
reinforcement learning, proposing a novel model that simplifies Motion
Imitation into a prediction problem of joint angle values in reinforcement
learning. This significantly reduces the reliance on vast amounts of training
data, enabling the agent to learn an imitation policy from just a few seconds
of video and exhibit strong generalization capabilities. It can quickly apply
the learned policy to imitate human arm motions in unfamiliar videos. The model
first extracts skeletal motions of human arms from a given video using 3D human
pose estimation. These extracted arm motions are then morphologically
retargeted onto a robotic manipulator. Subsequently, the retargeted motions are
used to generate reference motions. Finally, these reference motions are used
to formulate a reinforcement learning problem, enabling the agent to learn a
policy for imitating human arm motions. This project excels at imitation tasks
and demonstrates robust transferability, accurately imitating human arm motions
from other unfamiliar videos. This project provides a lightweight, convenient,
efficient, and accurate Motion Imitation model. While simplifying the complex
process of Motion Imitation, it achieves notably outstanding performance.
