In open-vocabulary mobile manipulation (OVMM), task success often hinges on the selection of an appropriate base placement for the robot. Existing approaches typically navigate to proximity-based regions without considering affordances, resulting in frequent manipulation failures. We propose Affordance-Guided Coarse-to-Fine Exploration, a zero-shot framework for base placement that integrates semantic understanding from vision-language models (VLMs) with geometric feasibility through an iterative optimization process. Our method constructs cross-modal representations, namely Affordance RGB and Obstacle Map+, to align semantics with spatial context. This enables reasoning that extends beyond the egocentric limitations of RGB perception. To ensure interaction is guided by task-relevant affordances, we leverage coarse semantic priors from VLMs to guide the search toward task-relevant regions and refine placements with geometric constraints, thereby reducing the risk of convergence to local optima. Evaluated on five diverse open-vocabulary mobile manipulation tasks, our system achieves an 85% success rate, significantly outperforming classical geometric planners and VLM-based methods. This demonstrates the promise of affordance-aware and multimodal reasoning for generalizable, instruction-conditioned planning in OVMM.

翻译：在开放词汇移动操作（OVMM）中，任务成功往往取决于为机器人选择合适的基座放置位置。现有方法通常导航到基于接近度的区域，而未考虑可供性，导致频繁的操作失败。我们提出了基于可供性引导的从粗到精探索，这是一种用于基座放置的零样本框架，它通过迭代优化过程，将来自视觉语言模型（VLM）的语义理解与几何可行性相结合。我们的方法构建了跨模态表示，即可供性RGB图和障碍物增强图，以将语义与空间上下文对齐。这使得推理能够超越RGB感知的自我中心限制。为确保交互由任务相关的可供性引导，我们利用来自VLM的粗略语义先验来引导搜索朝向任务相关区域，并通过几何约束细化放置位置，从而降低收敛到局部最优的风险。在五个不同的开放词汇移动操作任务上进行评估，我们的系统实现了85%的成功率，显著优于经典的几何规划器和基于VLM的方法。这证明了可供性感知和多模态推理在OVMM中实现可泛化的、指令条件规划的前景。