Bench-NPIN：非抓取交互导航的基准测试
Bench-NPIN: Benchmarking Non-prehensile Interactive Navigation
AI总结：
Bench-NPIN是首个非抓取交互导航的开源基准测试，提供多种模拟环境、评估指标和基线方法演示，用于提升可重复性和跨环境比较。
摘要：
移动机器人越来越多地部署在无结构环境中，其中障碍物和物体是可移动的。在这样的环境中进行导航被称为交互导航，任务完成不仅需要避开障碍物，还需要战略性地与可移动物体互动。非抓取交互导航专注于非抓取互动策略，如推动，而不是依赖于抓取操作。尽管该领域已有大量研究，但大多数解决方案都是使用特定案例设置进行评估，限制了可重复性和跨比较。在本文中，我们提出了Bench-NPIN，这是首个全面的非抓取交互导航基准测试。Bench-NPIN包含多个组件：1）一系列全面的模拟环境用于非抓取交互导航任务，包括导航有可移动障碍物的迷宫、在冰水中自主船舶导航、箱子交付和区域清理，每个任务都有不同复杂程度；2）一套评估指标，捕捉交互导航的独特方面，如效率、互动努力和部分任务完成；3）使用Bench-NPIN演示来评估现有基线方法在不同环境中的示例实现。Bench-NPIN是一个开源的Python库，具有模块化设计。代码、文档和训练模型可在https://github.com/IvanIZ/BenchNPIN找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
