# SG-CPG算法实现四足机器人95%关节损伤下自适应步态维持

> 原文链接：https://robotics.hellobike.com/papers/2026-09-22-2609-25687
> 频道：papers | 更新日期：2026-09-22 | 标签：auto, arxiv
> 核心摘要：针对执行器连续退化问题，SG-CPG通过残差与幅值双门控机制重构健康CPG策略。Unitree Go2实测显示，该算法在93%小腿扭矩损失下完成28/29次运动试验，仿真中95%强度损失时指令跟踪误差低于8%。

# SG-CPG算法实现四足机器人95%关节损伤下自适应步态维持

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[SG-CPG: Severity-Gated Central Pattern Generators for Adaptive Quadruped Locomotion under Continuous Actuator Degradation](https://arxiv.org/abs/2609.25687)（arXiv 2609.25687）

## 核心内容

受生物肢体受损后分级适应行为启发，研究提出SG-CPG算法解决四足机器人执行器连续退化难题。该方法保留冻结的健康CPG策略，引入严重度驱动的残差门控与幅值门控，分别负责四肢重协调与弱化腿步幅缩放。在Unitree Go2仿真测试中，即便关节强度损失达95%，系统仍保持100%存活率且全向指令跟踪误差控制在8%以内；真机验证中，在小腿扭矩退化高达93%的极端工况下，成功完成29次前进与转弯试验中的28次，证实了非离散故障处理的有效性。

技术层面通过降低关节扭矩上限与缩放底层控制器增益两种机制模拟执行器渐进式弱化。消融实验表明，在80%扭矩损失下移除残差观测或幅值门控，会导致弱化关节剪切率从4.4%分别激增至13.6%和26.3%。SG-CPG核心在于将故障视为连续变量而非离散状态，利用严重度门控动态调节健康策略输出，避免了传统方法在故障阈值切换时的步态震荡，为四足机器人在硬件老化或部分失效场景下的鲁棒控制提供了新范式。

## 关键看点

- **双门控机制**：引入残差门控重协调四肢、幅值门控缩放弱化腿步幅，基于严重度连续调节健康CPG策略输出
- **极限性能指标**：仿真中95%关节强度损失下存活率100%、指令跟踪误差<8%；真机93%小腿扭矩退化下试验成功率96.5%
- **消融实验验证**：80%扭矩损失下移除任一严重度路径，弱化关节剪切率由4.4%升至13.6%或26.3%，证明双通道必要性
- **退化建模方法**：采用降低扭矩上限与缩放底层增益双重机制模拟执行器连续弱化，区别于传统离散故障模型

## 工程与产业观察

SG-CPG将执行器故障从离散事件重构为连续严重度变量，通过门控机制复用健康策略，显著降低了对海量故障样本的依赖。该思路为具身智能本体在硬件老化、意外损伤等非结构化场景下的长周期可靠运行提供了轻量级控制层解决方案，对提升四足机器人野外作业鲁棒性具有直接工程参考价值。

## 英文摘要 (Abstract)

An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scales its response with injury severity. This graded adaptation allows locomotion to persist despite partial loss of limb strength, rather than requiring a discrete transition between healthy and failed. Inspired by this behavior, we propose SG-CPG, a central pattern generator (CPG) for quadruped locomotion under continuous actuator degradation. SG-CPG preserves a frozen healthy CPG policy and introduces two severity-driven gates: a residual gate that re-coordinates all four legs and an amplitude gate that progressively shortens the weakened leg's stride as degradation increases. We emulate progressive degradation through two mechanisms: lowering the joint torque ceiling (ceiling mechanism) and scaling its low-level controller gains (gain mechanism), representing distinct forms of actuator weakening. Our simulations on a Unitree Go2 show that SG-CPG maintains a trot gait with 100% survival across an omnidirectional command schedule under 95% joint strength loss while tracking commands within 8%. Under a lowered torque ceiling, removing either severity path, the residual's severity observation or the amplitude gate, raises clipping at the weakened joint from 4.4% to 13.6% and 26.3% of steps at an 80% loss. On a real Go2, SG-CPG survives 28 of 29 forward and turning trials with up to 93% calf torque degradation. These results show that severity-gated adaptation can extend a healthy locomotion policy to progressive actuator degradation without treating the fault as a discrete failure.

作者：Adarsh Kumar Kosta, Kaushik Roy
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## 原始参考来源 (Sources)

- [arXiv 2609.25687：SG-CPG: Severity-Gated Central Pattern Generators for Adaptive Quadruped Locomotion under Continuous Actuator Degradation](https://arxiv.org/abs/2609.25687)