Dribble Master: 通过腿部运动学习敏捷的人形机器人控球
Dribble Master: Learning Agile Humanoid Dribbling through Legged Locomotion
AI总结：
本文提出一种两阶段课程学习框架，使人形机器人无需显式动力学或预设轨迹即可掌握控球技能，通过模拟虚拟相机和启发式奖励提升球感知能力，实验证明其在多种环境中实现灵活美观的控球行为。
摘要：
人形足球控球是一项极具挑战性的任务，要求在保持动态平衡的同时进行灵活的球操控。传统基于规则的方法由于依赖固定行走模式和对实时球动态适应性差，难以实现精确的球控制。为此，我们提出一种两阶段课程学习框架，使人形机器人无需显式动力学或预设轨迹即可获得控球技能。第一阶段，机器人学习基本运动技能；第二阶段，我们微调策略以执行敏捷的控球动作。我们进一步引入模拟中的虚拟相机模型，模拟真实机器人视野和感知限制，使训练期间能实现逼真的球感知。我们还设计了启发式奖励，鼓励主动感知，促进连续的球感知范围扩大。策略在模拟中训练，并成功转移到物理人形机器人上。实验结果表明，我们的方法实现了有效的球操控，能够在多种环境中实现灵活且视觉上吸引人的控球行为。本工作展示了强化学习在开发敏捷人形足球机器人方面的潜力。更多细节和视频可在https://zhuoheng0910.github.io/dribble-master/上找到。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
