发表机构
Sapienza University of Rome; Institut de Robòtica i Informàtica Industrial (CSIC-UPC); École Polytechnique Fédérale de Lausanne (EPFL)（罗马第一大学; 工业机器人与信息学研究所（西班牙科学与技术研究委员会 - 加泰罗尼亚理工大学）; 洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人形机器人基于视觉的运球问题，提出将时间深度编码器嵌入强化学习策略的集成方法，应用于模拟机器人，该方法在不同场景下实现了一定成功率的运球，为处理复杂运球场景奠定基础。
AI 中文摘要
人形机器人技术的最新进展凸显了可部署的移动操作技能的重要性。在躲避活跃对手的同时运球需要在机载传感和实时约束下同时保持平衡、精确控球以及对动态对手的感知。现有方法通常将感知和运动分开，在有遮挡、快速球运动和复杂对手交互的情况下可能会失败。我们提出一种集成方法，通过特定任务投影层将时间深度编码器嵌入强化学习策略中。将此框架应用于模拟的Booster T1人形机器人，结果表明可以直接从深度观测中学习基于视觉、对手感知的运球，无需显式状态估计或特权场景信息。在名义目标驱动运球中学习到的策略成功率达到100%，有单个静态障碍物时为96%，面对主动移动的球攻击对手时为46%。这些结果表明所提出的框架在名义和适度动态设置中支持基于视觉的稳健运球，并为处理更具挑战性的移动对手场景奠定了坚实基础。
英文摘要
Recent advances in humanoid robotics have highlighted the importance of deployable loco-manipulation skills. Dribbling a soccer ball while evading active opponents requires simultaneous balance, precise ball control, and awareness of a dynamic adversary under onboard sensing and real-time constraints. Existing approaches typically separate perception and motion, which can be effective in controlled settings but may fail under occlusions, fast ball movements, and complex opponent interactions, since perception is not directly optimized for control. We propose an integrated approach in which a temporal depth encoder is embedded into a reinforcement learning policy through a task-specific projection layer. We apply this framework to a simulated Booster T1 humanoid robot and show that it is possible to learn vision-based, opponent-aware dribbling directly from depth observations, without explicit state estimation or privileged scene information. The learned policy achieves 100% success in nominal target-driven dribbling and 96% success with a single static obstacle, while reaching 46% success against an actively moving ball-attacker opponent. These results demonstrate that the proposed framework supports robust vision-based dribbling in nominal and moderately dynamic settings, and provides a strong foundation for handling more challenging moving-adversary scenarios.
