面向四足机器人滑板骑行的相位感知策略学习
Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots via Feature-wise Linear Modulation
AI总结：
本文提出相位感知策略学习框架，通过特征级线性调制层提升四足机器人滑板骑行的相位依赖行为捕捉与跨相位知识共享，验证了在仿真中的指令跟踪精度和效率。
摘要：
滑板作为一种紧凑高效的个人出行设备，但用腿部机器人控制其存在挑战，因为策略学习需要处理感知驱动的交互和多模态控制目标。为此，我们引入了相位感知策略学习（PAPL），一种针对四足机器人滑板骑行的强化学习框架。PAPL利用滑板的循环性质，通过在动作器和评价器网络中整合相位条件的特征级线性调制层，实现统一的策略，以捕捉相位依赖行为的同时共享机器人特定知识。我们的仿真评估验证了命令跟踪精度，并通过消融研究量化了每个组件的贡献。我们还比较了运动效率与腿部和轮腿基线，并展示了现实世界的可迁移性。
英文摘要：
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.
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。
