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.

翻译：现有的运动模仿模型通常需要借助动作捕捉设备获取专家数据，但训练所需的海量数据难以获取，需要投入大量资金、人力和时间。本项目将三维人体姿态估计与强化学习相结合，提出了一种新颖模型，将运动模仿简化为强化学习中的关节角度值预测问题，显著降低了对海量训练数据的依赖，使智能体仅需数秒视频即可学习模仿策略，并展现出强大的泛化能力——能够快速将所学策略应用于模仿陌生视频中的人类手臂运动。该模型首先通过三维人体姿态估计从给定视频中提取人类手臂骨骼运动，接着将这些提取的手臂运动通过形态重定向映射至机械臂模型，随后利用重定向后的运动生成参考动作，最后将这些参考动作用于构建强化学习问题，使智能体掌握模仿人类手臂运动的策略。本项目在模仿任务中表现优异，并展现出稳健的可迁移性，能够准确模仿其他陌生视频中的人类手臂运动。本项目提供了一种轻量化、便捷、高效且精准的运动模仿模型，在简化运动模仿复杂流程的同时，实现了卓越的性能表现。