# 通过基于采样的扰动观测器应对 Sim-to-Real 不匹配：从解析模型到学习型世界模型

> 原文链接：https://robotics.hellobike.com/papers/2026-10-04-2610-04896
> 频道：papers | 更新日期：2026-10-04 | 标签：arxiv, cs.RO
> 核心摘要：该文针对物理部署偏离标称模型的问题，提出 SDOB，并在仿真和真机实验中验证其补偿 Sim-to-Real 不匹配。

# 通过基于采样的扰动观测器应对 Sim-to-Real 不匹配：从解析模型到学习型世界模型

::: v-pre
> 原文链接：[Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models](https://arxiv.org/abs/2610.04896)（arXiv 2610.04896 · 发布日期：2026-10-04）

**Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models**

该文针对物理部署偏离标称模型的问题，提出 SDOB，并在仿真和真机实验中验证其补偿 Sim-to-Real 不匹配。

## 核心要点

- 机器人控制器依赖多类模型，但物理部署可能偏离标称假设并出现额外扰动。
- SDOB 将 DOB 原理扩展到仿真器和学习型世界模型，使用状态展开或代价查询接口。
- 方法区分状态效应扰动与代价扰动，分别对应反馈状态或查询代价的差异。
- 多种仿真和真机实验表明，SDOB 可补偿 Sim-to-Real 不匹配并改善控制性能。

**验证范围与局限**：验证范围为多种仿真与真机实验，覆盖传统模型和学习型模型；摘要未说明具体任务或机器人数量。

## 英文摘要 (Abstract)

Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.

作者：Tianqi Zhu, Jun Yang, Jianliang Mao, Cong Li, Shihua Li
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## 原始参考来源 (Sources)

- [arXiv 2610.04896：Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models](https://arxiv.org/abs/2610.04896)