CleanUpBench: Embodied Sweeping and Grasping Benchmark
Wenbo Li, Guanting Chen, Tao Zhao, Jiyao Wang, Tianxin Hu, Yuwen Liao, Weixiang Guo, and Shenghai Yuan

TL;DR
CleanUpBench is a new benchmark for evaluating embodied mobile cleaning robots with sweeping and grasping capabilities in realistic indoor environments, addressing a gap between research and real-world deployment.
Contribution
It introduces a reproducible, extensible benchmark built on NVIDIA Isaac Sim for assessing embodied agents in structured cleaning tasks, including environments, evaluation metrics, and baseline agents.
Findings
Benchmark enables systematic evaluation of cleaning robots.
Baseline agents demonstrate the platform's utility for comparison.
Supports generalization assessment with procedural environments.
Abstract
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…
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