Peg-in-Bench: A Modular Benchmark for High-Precision Robotic Insertion

2026-09-01

AI Summary

为解决高精度装配任务中的鲁棒性和泛化能力评估问题，本文提出一种可重构的模块化基准测试平台，通过3D打印组件和场景生成工具实现多样化任务配置。

Abstract

High-precision insertion remains a fundamental challenge in robotic manipulation due to the strict alignment requirements and contact-rich interactions involved. Although peg-in-hole tasks are widely used for evaluation, existing bench- marks often rely on fixed task configurations, limiting their ability to assess robustness and generalization across different insertion scenarios. This paper introduces a reconfigurable peg-in-hole benchmark designed to evaluate task generalization in high-precision insertion. The benchmark consists of a set of fully 3D-printable modular components, including multiple peg geometries, tolerance levels, and configurable base structures that can be combined to generate a large variety of insertion and assembly tasks. By varying object layouts, orientations, and task structures while maintaining controlled physical conditions, the benchmark enables systematic evaluation of adaptation to unseen scenarios. To support reproducibility, we additionally provide a scenario generation tool capable of producing standardized task configurations and machine-readable task descriptions. The scenario generation tool and the STL files of the benchmark pieces are available through the project repository: https://github.com/aistairc/peg-in-bench.

Problem

• high-precision insertion
• robotic manipulation
• task generalization
• peg-in-hole

Innovation

• reconfigurable peg-in-hole benchmark
• modular components
• task generalization
• scenario generation tool