Abstract
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.
Abstract (translated)
在像羽毛球这样的高需求体育项目中，实现多功能和人类般的表现仍然是人形机器人的重大挑战。与标准行走或静态操作不同，这项任务需要全身协调的爆发力以及精确、时间敏感性的拦截能力。尽管最近的进步已经实现了逼真的动作模仿，但要在不牺牲风格自然性的情况下从姿态模拟过渡到功能性和物理感知的击球则是非同寻常的困难。为了解决这一问题，我们提出了一种名为“模仿至互动”的逐步强化学习框架，旨在将机器人从“模仿者”进化成一个有能力的“打击者”。我们的方法通过人体数据建立了稳健的运动先验，并将其提炼成了一个紧凑、模型为基础的状态表示形式。此外，我们还通过对抗性先验稳定了动力学性能。至关重要的是，为了克服专家演示稀疏性的难题，我们引入了一种流形扩展策略，将离散打击点推广到密集互动空间。 我们在模拟中通过掌握多样化的技能，包括挑高球和低手杀球来验证我们的框架的有效性。此外，我们首次展示了从仿真环境到现实世界的人类形态羽毛球技能的零样本迁移，成功地在物理环境中再现了人类运动员的动态优雅性和功能精确度。
URL
https://arxiv.org/abs/2602.08370
