# 用于将插销插入可移动孔的视觉-力导纳学习

> 原文链接：https://robotics.hellobike.com/papers/2026-09-17-2609-14133
> 频道：papers | 更新日期：2026-09-17 | 标签：arxiv, cs.RO
> 核心摘要：针对动态环境下插孔装配精度与不确定性冲突，VFAL融合视觉与力导纳模型，并在真实实验验证。

# 用于将插销插入可移动孔的视觉-力导纳学习

::: v-pre
> 原文链接：[Vision-Force Admittance Learning for Peg Insertion Into a Movable Hole](https://arxiv.org/abs/2609.14133)（arXiv 2609.14133 · 发布日期：2026-09-17）

**Vision-Force Admittance Learning for Peg Insertion Into a Movable Hole**

针对动态环境下插孔装配精度与不确定性冲突，VFAL融合视觉与力导纳模型，并在真实实验验证。

## 核心要点

- 动态环境会引入显著不确定性，与插孔等精密任务的高精度要求冲突。
- VFAL融合异步视觉反馈和高频基于力的模型，以视觉位姿估计作为正则。
- 利用视觉基础模型获取稳健低频位姿信息，并加入失败恢复机制。
- 真实实验显示该方法成功率高，并能适应多种插销和动态环境。

**验证范围与局限**：验证范围为真实世界实验中的多种插销和动态环境。

## 英文摘要 (Abstract)

Precise manipulation in dynamic environments, whether induced by a mobile robot base or a target with unknown motion, remains a major challenge in robotics. Manipulation in dynamic environments introduces substantial uncertainty, which fundamentally conflicts with the tight precision requirement of precise tasks such as peg-in-the-hole. We propose a Vision-Force Admittance Learning (VFAL) framework that fuses asynchronous visual feedback with a high-frequency force-based model, using visual pose estimations as a regularization term. VFAL adapts insertion strategies online to dynamic motion while maintaining millimeter-level precision. To obtain robust, low-frequency pose information, we employ state-of-the-art vision foundation models for visual pose estimation. Additionally, we incorporate failure recovery mechanisms to enhance overall robustness. We validate our approach in real-world experiments, demonstrating high success rates and strong adaptability to various pegs and dynamic environments.

作者：Yuzhong Chen, Yongqing Liang, Yunzhi Xu, Irving Fang, Chase Kidder, Hui-Ping Wang, Raihan Haque, Yubiao Zhang, Chen Feng
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## 原始参考来源 (Sources)

- [arXiv 2609.14133：Vision-Force Admittance Learning for Peg Insertion Into a Movable Hole](https://arxiv.org/abs/2609.14133)