Peg-in-Bench：用于高精度机器人插入的模块化基准测试集
Peg-in-Bench: A Modular Benchmark for High-Precision Robotic Insertion
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中文总结 AI 辅助
本文提出Peg-in-Bench模块化基准测试集，可生成多样轴孔插入任务以评估机器人高精度插入的泛化能力，配套场景生成工具保障结果可复现。
中文摘要 AI 辅助
高精度插入因涉及严格的对齐要求和大量接触交互，仍是机器人操作领域的基础挑战。尽管轴孔插入任务被广泛用于评估，但现有基准测试集通常依赖固定的任务配置，限制了其评估不同插入场景下鲁棒性和泛化能力的效果。本文提出一种可重构轴孔插入基准测试集，用于评估高精度插入中的任务泛化能力。该基准测试集包含一组完全可3D打印的模块化组件，包括多种轴几何形状、公差等级以及可配置的基座结构，这些组件可组合生成大量不同的插入与装配任务。通过在保持可控物理条件的同时改变物体布局、朝向和任务结构，该基准测试集可实现对未知场景适应能力的系统评估。为支持可复现性，本文还提供了一种场景生成工具，能够生成标准化任务配置和机器可读的任务描述。该场景生成工具及基准组件的STL文件可通过项目仓库获取：this https URL。
英文摘要
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.
发表机构
- National Institute of Advanced Industrial Science and Technology (AIST)（日本国立 Advanced Industrial Science and Technology 研究所（AIST））
机构由 AI 辅助整理，请以论文原文为准。
