Mobile robots are increasingly deployed in unstructured environments where obstacles and objects are movable. Navigation in such environments is known as interactive navigation, where task completion requires not only avoiding obstacles but also strategic interactions with movable objects. Non-prehensile interactive navigation focuses on non-grasping interaction strategies, such as pushing, rather than relying on prehensile manipulation. Despite a growing body of research in this field, most solutions are evaluated using case-specific setups, limiting reproducibility and cross-comparison. In this paper, we present Bench-NPIN, the first comprehensive benchmark for non-prehensile interactive navigation. Bench-NPIN includes multiple components: 1) a comprehensive range of simulated environments for non-prehensile interactive navigation tasks, including navigating a maze with movable obstacles, autonomous ship navigation in icy waters, box delivery, and area clearing, each with varying levels of complexity; 2) a set of evaluation metrics that capture unique aspects of interactive navigation, such as efficiency, interaction effort, and partial task completion; and 3) demonstrations using Bench-NPIN to evaluate example implementations of established baselines across environments. Bench-NPIN is an open-source Python library with a modular design. The code, documentation, and trained models can be found at https://github.com/IvanIZ/BenchNPIN.

翻译：移动机器人越来越多地被部署在非结构化环境中，这些环境中的障碍物和物体是可移动的。在此类环境中的导航被称为交互式导航，其任务完成不仅需要避开障碍物，还需要对可移动物体进行策略性交互。非抓取式交互导航侧重于非抓取的交互策略，例如推动，而非依赖抓取式操作。尽管该领域的研究日益增多，但大多数解决方案都使用特定案例的设置进行评估，限制了可重复性和交叉比较。本文提出了Bench-NPIN，这是首个针对非抓取式交互导航的综合性基准测试。Bench-NPIN包含多个组成部分：1）一系列全面的非抓取式交互导航任务模拟环境，包括具有可移动障碍物的迷宫导航、冰域中的自主船舶导航、箱体递送和区域清理，每个任务均具有不同的复杂度级别；2）一套评估指标，用于捕捉交互式导航的独特方面，如效率、交互努力程度和部分任务完成度；以及3）使用Bench-NPIN对跨环境的现有基线方法示例实现进行评估的演示。Bench-NPIN是一个采用模块化设计的开源Python库。代码、文档和训练模型可在 https://github.com/IvanIZ/BenchNPIN 找到。