Vision-Force Admittance Learning for Peg Insertion into a Movable Hole

2026-09-12

AI Summary

为解决动态环境中精确操作问题，提出视觉-力顺应性学习框架VFAL，融合视觉反馈与力模型，实现高精度适应性插入。

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.

Problem

• dynamic environments
• precise manipulation
• peg-in-the-hole
• uncertainty
• visual feedback

Innovation

• Vision-Force Admittance Learning
• asynchronous visual feedback
• dynamic environments
• failure recovery mechanisms