Abstract
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 Hamlet, 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 e.g., agile catching, table tennis.
Framework
We first detect and predict the incoming shuttlecock’s trajectory, then drive the chassis with a model‑based PD controller to a reachable base position. The predicted trajectory is rigidly transformed—translated by that base position and rotated according to the chassis orientation—into the local arm coordinate frame. Finally, a learning‑based policy uses the transformed trajectory to generate precise end‑effector velocity commands for accurate hitting.
