让物理模拟足球球员听用户指挥，流畅切换各种动作。
PhysicsFC: Learning User-Controlled Skills for a Physics-Based Football Player Controller
- 用特定策略生成动作潜变量，结合物理运动嵌入模型控制角色。
- 训练出可自然切换的控球、停球、移动等技能，支持11人对11人游戏。
- 适合做交互式足球仿真，尤其看重动作连贯性的研究者。
我们提出PhysicsFC，一种基于用户输入控制物理模拟足球角色的方法，使其能够执行多种足球技能（如带球、停球、移动和踢球），并实现技能间的无缝切换。每个技能采用专用策略生成潜变量，依托现有物理驱动的动作嵌入模型作为基础以复现真实足球动作。关键设计包括：为带球策略定制奖励函数，停球策略采用两阶段奖励结构与抛体动力学初始化，移动策略使用数据嵌入的目标条件潜向量引导（DEGCL）。通过训练好的技能策略，构建足球角色有限状态机（PhysicsFC FSM），支持用户交互控制。为确保状态间平滑过渡，引入技能切换初始化（STI）方法，在各策略训练中应用。我们设计了多种交互场景展示效果，包括对抗性停球与带球、二过一配合及11人对11人比赛，多个PhysicsFC智能体展现出自然且可控的物理化行为。定量评估验证了各技能策略及其转换性能，使用了指定指标与实验设计。
原文摘要 · Abstract (English)
We propose PhysicsFC, a method for controlling physically simulated football player characters to perform a variety of football skills--such as dribbling, trapping, moving, and kicking--based on user input, while seamlessly transitioning between these skills. Our skill-specific policies, which generate latent variables for each football skill, are trained using an existing physics-based motion embedding model that serves as a foundation for reproducing football motions. Key features include a tailored reward design for the Dribble policy, a two-phase reward structure combined with projectile dynamics-based initialization for the Trap policy, and a Data-Embedded Goal-Conditioned Latent Guidance (DEGCL) method for the Move policy. Using the trained skill policies, the proposed football player finite state machine (PhysicsFC FSM) allows users to interactively control the character. To ensure smooth and agile transitions between skill policies, as defined in the FSM, we introduce the Skill Transition-Based Initialization (STI), which is applied during the training of each skill policy. We develop several interactive scenarios to showcase PhysicsFC's effectiveness, including competitive trapping and dribbling, give-and-go plays, and 11v11 football games, where multiple PhysicsFC agents produce natural and controllable physics-based football player behaviors. Quantitative evaluations further validate the performance of individual skill policies and the transitions between them, using the presented metrics and experimental designs.
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