CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly

TL;DR

This paper introduces CFVS, a robust visual servoing method for 6-DoF peg-in-hole assembly that handles large initial errors and arbitrary tilt angles, outperforming existing approaches in success rates.

Contribution

The paper proposes a novel coarse-to-fine visual servoing approach with a confidence map for robust 6-DoF peg-in-hole assembly, addressing limitations of prior methods.

Findings

Achieves 100% success in 3-DoF assembly

Attains 91% success in 4-DoF assembly

Reaches 82% success in 6-DoF assembly

Abstract

Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end-effector, or limiting the distance between the target and the initial pose position, which prevents them from being deployed in real-world manufacturing. Thus, we present a Coarse-to-Fine Visual Servoing (CFVS) peg-in-hole method, achieving 6-DoF end-effector motion control based on 3D visual feedback. CFVS can handle arbitrary tilt angles and large initial alignment errors through a fast pose estimation before refinement. Furthermore, by introducing a confidence map to ignore the irrelevant contour of objects, CFVS is robust against noise and can deal with various targets beyond training data. Extensive experiments show CFVS outperforms state-of-the-art methods and obtains 100%, 91%, and 82% average success rates in 3-DoF, 4-DoF, and 6-DoF peg-in-hole, respectively.