让双足足球机器人自适应切换任务，稳定踢球与跌倒恢复
Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
- 分层控制：前馈振荡器生成步态，强化学习反馈优化动作
- 平均0.715秒内完成跌倒自恢复，角落场景仍能精准踢球
- 状态机驱动任务切换，避免不同行为相互干扰，适合复杂环境
在动态对抗环境中开发双足足球机器人面临运动稳定性差、多任务深度耦合及状态切换难题。本文提出一种模块化强化学习框架，实现自适应多任务控制。首先，将开环前馈振荡器与基于强化学习的反馈残差策略结合，有效分离基础步态与复杂足球动作的生成。其次，引入基于姿态的状态机，明确切换球寻找与踢球网络（BSKN）和跌倒恢复网络（FRN），从根本上避免状态干扰。通过渐进式力衰减课程学习策略高效训练FRN。该架构在Unity仿真中验证，展现出优异的空间适应性——即使在受限角落也能可靠寻球并踢球；且跌倒后可快速自主恢复，平均恢复时间仅0.715秒，确保复杂多任务环境下无缝稳定运行。
原文摘要 · Abstract (English)
Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, as well ascontrol switching issues between different states such as up-right walking and fall recovery. To address these problems,this paper proposes a modular reinforcement learning (RL)framework for achieving adaptive multi-task control. Firstly,this framework combines an open-loop feedforward oscilla-tor with a reinforcement learning-based feedback residualstrategy, effectively separating the generation of basic gaitsfrom complex football actions. Secondly, a posture-driven statemachine is introduced, clearly switching between the ballseeking and kicking network (BSKN) and the fall recoverynetwork (FRN), fundamentally preventing state interference.The FRN is efficiently trained through a progressive forceattenuation curriculum learning strategy. The architecture wasverified in Unity simulations of bipedal robots, demonstratingexcellent spatial adaptability-reliably finding and kicking theball even in restricted corner scenarios-and rapid autonomousfall recovery (with an average recovery time of 0.715 seconds).This ensures seamless and stable operation in complex multi-task environments.
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