用轮滑步态让机器人更省力、更耐用，减少75%冲击力。
SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles
- 给机器人脚部加四轮，用深度强化学习训练轮滑步态。
- 实测冲击强度降75.86%，能耗降低63.34%。
- 适合追求高效长续航的足式机器人研究者。
尽管近年类人机器人在行走和奔跑方面取得显著进展，但频繁的脚部着地会产生高瞬时冲击力，导致关节磨损加剧且能量利用效率低。轮滑运动具有显著生物力学价值，可通过合理利用身体惯性实现快速连续滑行，动能损耗极小。为此，本研究提出一种新型类人机器人，每只脚配备一排四个被动轮用于轮滑。同时开发了基于轮滑内在特性的深度强化学习控制框架，设计奖励函数以实现滑行步态优化。所学策略先在仿真中分析，再部署于物理机器人，验证其在冲击强度与运输成本上均优于传统双足步行。实验结果显示，两项指标分别降低75.86%和63.34%，表明轮滑是提升能效与关节寿命的更优移动方式。
原文摘要 · Abstract (English)
Although recent years have seen significant progress of humanoid robots in walking and running, the frequent foot strikes with ground during these locomotion gaits inevitably generate high instantaneous impact forces, which leads to exacerbated joint wear and poor energy utilization. Roller skating, as a sport with substantial biomechanical value, can achieve fast and continuous sliding through rational utilization of body inertia, featuring minimal kinetic energy loss. Therefore, this study proposes a novel humanoid robot with each foot equipped with a row of four passive wheels for roller skating. A deep reinforcement learning control framework is also developed for the swizzle gait with the reward function design based on the intrinsic characteristics of roller skating. The learned policy is first analyzed in simulation and then deployed on the physical robot to demonstrate the smoothness and efficiency of the swizzle gait over traditional bipedal walking gait in terms of Impact Intensity and Cost of Transport during locomotion. A reduction of $75.86\%$ and $63.34\%$ of these two metrics indicate roller skating as a superior locomotion mode for enhanced energy efficiency and joint longevity.
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