外观
论文arXiv 2610.0541424 天前
RMMBench：面向机器人移动操作的综合基准
原文链接：RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation（arXiv 2610.05414 · 发布日期：2026-10-04）
RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation
提出RMMBench评估VLM在连续空间中理解指令并执行导航-操作长程任务的具身能力。
核心要点
- 现有基准难以全面、细粒度评估VLM在多样机器人任务中的具身能力。
- RMMBench统一高低层具身任务，要求机器人理解语言指令并执行长程任务。
- 基准构建70个导航-操作典型场景，覆盖局部操作到长程复合导航。
- 结果显示领先VLM在移动操作中的空间定位仍面临重大挑战；项目已开放访问。
验证范围与局限：验证范围为连续空间中的70个导航-操作任务场景，摘要未说明仿真或真机环境。
英文摘要 (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/
作者：H. Li, Fuxiang Feng, 范金秋, Shuo Yang, Fengjiao Chen, Xuezhi Cao, Ran Song, Wei Emma Zhang
来源
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