Abstract
Although the advancement of vision-language models (VLMs) has endowed robots with enhanced environmental understanding and task reasoning, a comprehensive evaluation methodology is important to advance the integration of VLMs in robotic navigation and manipulation. However, current benchmarks lack a comprehensive method to evaluate diverse robotic tasks, and evaluation metrics remain relatively constrained, making it difficult to assess the embodied capabilities of VLMs in a thorough and fine-grained manner. To address this issue, we propose RMMBench, an evaluation benchmark that requires robots to understand language instructions and perform long-horizon tasks in continuous spaces. RMMBench seamlessly integrates high- and low-level embodied tasks into a unified framework, constructing a "navigation-manipulation" task suite comprising 70 canonical task scenarios that range from localized manipulation to long-horizon composite navigation. The results reveal that leading VLMs still face major challenges in spatial localization when performing mobile manipulation tasks, and also highlight the necessity of enhancing the spatial perception capability of robots during long-horizon interactions. RMMBench can be accessed at https://mxxq-stack.github.io/rmmbench-project/
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Cite this article
APA 7
Li, H., Feng, F., Fan, J., Yang, S., Chen, F., Cao, X., Song, R., & Zhang, W. (2026). RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation. https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation
MLA 9
Li, Huapeng, et al. "RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation." https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation.
Chicago (author–date)
Li, Huapeng, Fuxiang Feng, Jinqiu Fan, Shuo Yang, Fengjiao Chen, Xuezhi Cao, Ran Song, and Wei Zhang. 2026. "RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation." https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation.
Harvard
Li, H., Feng, F., Fan, J., Yang, S., Chen, F., Cao, X., Song, R. and Zhang, W. (2026) 'RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation', Available at: https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation.
Vancouver
Li H, Feng F, Fan J, Yang S, Chen F, Cao X, et al. RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation. https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation
IEEE
H. Li, F. Feng, J. Fan, S. Yang, F. Chen, X. Cao, R. Song, and W. Zhang, "RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation," https://omanscience.com/en/articles/rmmbench-a-comprehensive-benchmark-for-robotic-mobile-manipulation.