Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world.

翻译：在高强度运动（如羽毛球）中实现多样化且类人的表现，对人形机器人而言仍是一项艰巨挑战。与标准移动或静态操控不同，该任务要求爆发性的全身协调与精准、时机关键的击球动作实现无缝集成。尽管近期进展已实现逼真的运动模仿，但在不牺牲风格自然性的前提下，弥合运动学模仿与功能性、物理感知的击球之间的鸿沟并非易事。为此，我们提出“模仿到交互”——一种渐进式强化学习框架，旨在使机器人从“模仿者”进化为具备能力的“击球手”。我们的方法从人类数据中建立稳健的运动先验，将其提炼为紧凑的、基于模型的状态表示，并通过对抗性先验稳定动力学。关键的是，为克服专家演示数据的稀疏性，我们引入了一种流形扩展策略，将离散的击球点泛化为稠密的交互空间。我们通过在仿真中掌握多种技能（包括高远球和吊球）来验证所提框架。此外，我们首次展示了拟人化羽毛球技能从仿真到真实环境的零样本迁移，在物理世界中成功复现了人类运动员的动力学优雅性与功能精确性。