Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce \ourmethod, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed ``IL+RL'' training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and 90.7% success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Our project website: https://dreamstarring.github.io/HAMLET/.

本研究解决了现有技术中缺乏学习与模型相结合的策略，以简化训练复杂性并确保灵活羽毛球机器人控制的安全性和稳定性。提出了一种新颖的混合控制系统\ourmethod，其中通过模型驱动的底盘运动策略为臂部控制策略提供基础，并引入了一种物理信息驱动的“IL+RL”训练框架。实验证明，该系统在对抗发球机和人类选手时分别达到94.5%和90.7%的成功率，具有广泛的适用性。

结合基于学习的操作与基于物理的运动实现全身羽毛球机器人控制