首个统一基准，评估机器人推物导航与操作能力。
Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots
- 构建包含迷宫、冰海航行等场景的仿真环境。
- 设计效率、交互力度等新评估指标，支持部分完成任务衡量。
- 开源工具包，适合研究移动机器人推物策略的学者使用。
移动机器人在杂乱环境中越来越多地遇到可移动物体，传统避障方法难以应对。在此类场景中，机器人需采用推挤或轻推策略实现目标。尽管推物机器人研究日益增多，但评估依赖非标准化设置，导致可复现性差、跨方法比较困难。为此，我们提出 Bench-Push——首个针对推物式移动机器人导航与操作任务的统一基准。Bench-Push 包含三方面：1）涵盖迷宫导航、冰面船舶自主航行、箱子递送、区域清理等多种复杂度的仿真环境；2）新颖的评估指标，用于衡量任务效率、交互努力程度及部分任务完成情况；3）基于 Bench-Push 的基线模型演示，展示其在不同环境中的表现。Bench-Push 已开源为模块化 Python 库，代码、文档及训练模型见 https://github.com/IvanIZ/BenchNPIN。
原文摘要 · Abstract (English)
Mobile robots are increasingly deployed in cluttered environments with movable objects, posing challenges for traditional methods that prohibit interaction. In such settings, the mobile robot must go beyond traditional obstacle avoidance, leveraging pushing or nudging strategies to accomplish its goals. While research in pushing-based robotics is growing, evaluations rely on ad hoc setups, limiting reproducibility and cross-comparison. To address this, we present Bench-Push, the first unified benchmark for pushing-based mobile robot navigation and manipulation tasks. Bench-Push includes multiple components: 1) a comprehensive range of simulated environments that capture the fundamental challenges in pushing-based tasks, including navigating a maze with movable obstacles, autonomous ship navigation in ice-covered waters, box delivery, and area clearing, each with varying levels of complexity; 2) novel evaluation metrics to capture efficiency, interaction effort, and partial task completion; and 3) demonstrations using Bench-Push to evaluate example implementations of established baselines across environments. Bench-Push is open-sourced as a Python library with a modular design. The code, documentation, and trained models can be found at https://github.com/IvanIZ/BenchNPIN.
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