Humanoid soccer dribbling is a highly challenging task that demands dexterous ball manipulation while maintaining dynamic balance. Traditional rule-based methods often struggle to achieve accurate ball control due to their reliance on fixed walking patterns and limited adaptability to real-time ball dynamics. To address these challenges, we propose a two-stage curriculum learning framework that enables a humanoid robot to acquire dribbling skills without explicit dynamics or predefined trajectories. In the first stage, the robot learns basic locomotion skills; in the second stage, we fine-tune the policy for agile dribbling maneuvers. We further introduce a virtual camera model in simulation that simulates the field of view and perception constraints of the real robot, enabling realistic ball perception during training. We also design heuristic rewards to encourage active sensing, promoting a broader visual range for continuous ball perception. The policy is trained in simulation and successfully transferred to a physical humanoid robot. Experiment results demonstrate that our method enables effective ball manipulation, achieving flexible and visually appealing dribbling behaviors across multiple environments. This work highlights the potential of reinforcement learning in developing agile humanoid soccer robots. Additional details and videos are available at https://zhuoheng0910.github.io/dribble-master/.

翻译：人形机器人足球运球是一项极具挑战性的任务，要求在保持动态平衡的同时实现灵巧的控球操作。传统的基于规则的方法通常依赖固定的行走模式，且对实时球体动态的适应能力有限，因此难以实现精确的控球。为解决这些挑战，我们提出了一种两阶段课程学习框架，使人形机器人能够在无需显式动力学模型或预定义轨迹的情况下掌握运球技能。第一阶段，机器人学习基础的运动技能；第二阶段，我们对策略进行微调以实现敏捷的运球动作。我们进一步在仿真中引入了虚拟相机模型，模拟真实机器人的视野范围和感知约束，从而在训练过程中实现真实的球体感知。我们还设计了启发式奖励机制以鼓励主动感知，促进更广的视觉范围以实现连续的球体追踪。该策略在仿真环境中训练，并成功迁移至实体人形机器人。实验结果表明，我们的方法能够实现有效的控球操作，在多种环境中展现出灵活且视觉表现优异的运球行为。本研究凸显了强化学习在开发敏捷人形足球机器人方面的潜力。更多细节和视频请访问 https://zhuoheng0910.github.io/dribble-master/。