让四足机器人学会分阶段滑板，提升控制精度与适应性。
Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots via Feature-wise Linear Modulation
- 用分段感知的特征调制技术，让策略网络区分滑板不同阶段行为。
- 仿真测试中实现高精度指令追踪，实现在真实环境中的成功转移。
- 适合研究机器人复杂运动控制与分阶段强化学习的科研人员。
滑板作为一种个人移动设备，具有紧凑高效的特点。然而，用四足机器人操控滑板面临感知驱动交互和多模态控制目标带来的挑战。为此，我们提出相位感知策略学习（PAPL），一种专为四足机器人滑板设计的强化学习框架。PAPL利用滑板动作的周期性，将相位条件的逐特征线性调制层融入策略和评判网络，使单一策略能捕捉不同阶段的行为，并在各阶段间共享机器人特定知识。仿真评估验证了指令跟踪精度，并通过消融实验量化各组件贡献。我们还对比了运动效率与纯腿型及轮腿基线模型，展示了实际场景的可迁移性。
原文摘要 · Abstract (English)
Skateboards offer a compact and efficient means of transportation as a type of personal mobility device. However, controlling them with legged robots poses several challenges for policy learning due to perception-driven interactions and multi-modal control objectives across distinct skateboarding phases. To address these challenges, we introduce Phase-Aware Policy Learning (PAPL), a reinforcement-learning framework tailored for skateboarding with quadruped robots. PAPL leverages the cyclic nature of skateboarding by integrating phase-conditioned Feature-wise Linear Modulation layers into actor and critic networks, enabling a unified policy that captures phase-dependent behaviors while sharing robot-specific knowledge across phases. Our evaluations in simulation validate command-tracking accuracy and conduct ablation studies quantifying each component's contribution. We also compare locomotion efficiency against leg and wheel-leg baselines and show real-world transferability.
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