CleanUpBench：具身清扫与抓取基准
CleanUpBench: Embodied Sweeping and Grasping Benchmark
AI总结：
CleanUpBench是一个基于NVIDIA Isaac Sim的具身清洁基准，通过模拟带清扫机构和六自由度机械臂的移动机器人，评估多目标清扫与抓取任务，填补了学术研究与实际应用间的空白。
摘要：
具身人工智能基准已经推动了导航、操作和推理的发展，但大多数基准针对的是复杂的人形智能体或大规模仿真，这些场景远未达到实际部署的要求。相比之下，具有清扫和抓取等双模式能力的移动清洁机器人正迅速成为现实且具有商业可行性的平台。然而，目前尚无基准能够系统地评估这些智能体在结构化、多目标清洁任务中的表现，这揭示了学术研究与实际应用之间的关键差距。我们提出了CleanUpBench，这是一个可复现且可扩展的基准，用于在逼真的室内清洁场景中评估具身智能体。CleanUpBench基于NVIDIA Isaac Sim构建，模拟了一台配备清扫机构和六自由度机械臂的移动服务机器人，使其能够与异构物体进行交互。该基准包含人工设计的环境和一个程序化生成的布局，以评估泛化能力，并提供一套全面的评估套件，涵盖任务完成度、空间效率、运动质量和控制性能。为了支持对比研究，我们提供了基于启发式策略和地图规划的基线智能体。CleanUpBench弥合了低级技能评估与全场景测试之间的差距，为日常环境中的落地具身智能提供了一个可扩展的测试平台。
英文摘要：
Embodied AI benchmarks have advanced navigation, manipulation, and reasoning, but most target complex humanoid agents or large-scale simulations that are far from real-world deployment. In contrast, mobile cleaning robots with dual mode capabilities, such as sweeping and grasping, are rapidly emerging as realistic and commercially viable platforms. However, no benchmark currently exists that systematically evaluates these agents in structured, multi-target cleaning tasks, revealing a critical gap between academic research and real-world applications. We introduce CleanUpBench, a reproducible and extensible benchmark for evaluating embodied agents in realistic indoor cleaning scenarios. Built on NVIDIA Isaac Sim, CleanUpBench simulates a mobile service robot equipped with a sweeping mechanism and a six-degree-of-freedom robotic arm, enabling interaction with heterogeneous objects. The benchmark includes manually designed environments and one procedurally generated layout to assess generalization, along with a comprehensive evaluation suite covering task completion, spatial efficiency, motion quality, and control performance. To support comparative studies, we provide baseline agents based on heuristic strategies and map-based planning. CleanUpBench bridges the gap between low-level skill evaluation and full-scene testing, offering a scalable testbed for grounded, embodied intelligence in everyday settings.
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。
