# SKATER：人形机器人轮滑Swizzle步态降低76%冲击与63%能耗

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2601-04948
> 频道：papers | 更新日期：2026-10-15 | 标签：auto, arxiv
> 核心摘要：研究提出足底四被动轮人形机器人及Swizzle步态强化学习框架，实机验证相比双足行走冲击强度降75.86%、运输成本降63.34%，显著提升能效与关节寿命。

# SKATER：人形机器人轮滑Swizzle步态降低76%冲击与63%能耗

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles](https://arxiv.org/abs/2601.04948)（arXiv 2601.04948）

## 核心内容

针对人形机器人双足行走地面冲击大、关节磨损严重及能效低的问题，研究团队提出SKATER方案。该方案在人形机器人单足集成四个被动滚轮，并开发基于深度强化学习的Swizzle（内刃外刃交替滑行）步态控制策略。仿真与真机部署结果表明，相比传统双足行走，该轮滑步态使冲击强度降低75.86%，运输成本（CoT）降低63.34%，验证了其在提升运动平滑度、能源利用率及机械结构耐久性方面的显著优势。

技术层面，系统采用无动力被动轮组设计以最小化动能损耗，利用身体惯性实现连续滑动。控制算法设计了契合轮滑生物力学特性的奖励函数，通过Sim-to-Real迁移将策略部署至物理样机。实验量化数据显示，Swizzle步态有效规避了频繁触地带来的瞬时高冲击力，解决了刚性接触导致的执行器寿命瓶颈。该工作为具身智能移动底盘提供了“轮式效率+双足地形适应性”的融合新范式，拓展了人形机器人在平整场景下的高效长续航作业能力。

## 关键看点

- **被动轮组硬件**：单足集成四个被动滚轮，利用惯性滑行替代主动驱动，从机械结构上消除行走触地冲击源
- **RL步态策略**：开发Swizzle步态专用强化学习框架，奖励函数基于轮滑生物力学特性设计，实现Sim-to-Real平滑迁移
- **能效与减振**：真机实测冲击强度较双足行走降低75.86%，运输成本CoT降低63.34%，大幅延长关节模组寿命
- **运动模态创新**：验证轮滑作为人形机器人高效移动模式的可行性，兼顾轮式低能耗与双足本体构型兼容性

## 工程与产业观察

SKETER证明了混合运动模态对具身智能工程化的价值。在结构化环境中，通过被动机构改造结合专用RL策略，可将人形机器人能效提升至接近轮式水平。这提示研发者：不必在所有场景强求纯双足行走，针对特定工况的硬件-算法协同设计（如被动轮、弹簧储能）是解决关节热失效与续航焦虑的高ROI路径，也为仓储物流等场景的人形机器人落地提供了差异化技术储备。

## 英文摘要 (Abstract)

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.

作者：Junchi Gu, Feiyang Yuan, Weize Shi, Tianchen Huang, Haopeng Zhang, Xiaohu Zhang, Yu Wang, Wei Gao, Shiwu Zhang
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## 原始参考来源 (Sources)

- [arXiv 2601.04948：SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles](https://arxiv.org/abs/2601.04948)