MoMa-Kitchen：用于移动操作中可供性锚定最后一英里导航的10万+样本基准
MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation
AI总结：
本文提出含10万余样本的 MoMa-Kitchen 基准，利用可供性标签训练移动操作器的最终导航定位，并给出轻量级 NavAff 基线。
摘要：
在移动操作中，导航与操作常被视为彼此独立的问题，导致仅仅接近物体与有效接触物体之间存在显著差距。许多导航方法主要以与目标的接近程度定义成功，往往忽视了有利于后续操作的最优定位需求。为解决这一问题，我们提出 MoMa-Kitchen，这是一个包含超过10万个样本的基准数据集，为模型学习最优最终导航位置、实现向操作的无缝过渡提供训练数据。该数据集包含从多样化厨房环境中采集的可供性锚定地面标签；在这些环境中，不同型号的机器人移动操作器尝试在杂乱场景中抓取目标物体。我们利用全自动化流程模拟多种真实场景，并为最优操作位置生成可供性标签。视觉数据来自安装在机械臂上的第一人称视角相机所采集的 RGB-D 输入，确保数据采集过程中的视角一致性。我们还开发了用于导航可供性锚定的轻量级基线模型 NavAff，并在 MoMa-Kitchen 基准上展示出良好性能。所提方法使模型能够学习基于可供性的最终定位，适配不同机械臂类型和平台高度，从而为具身智能中更鲁棒、更具泛化性的导航与操作融合铺平道路。项目页面：https://momakitchen.github.io/。
英文摘要：
In mobile manipulation, navigation and manipulation are often treated as separate problems, resulting in a significant gap between merely approaching an object and engaging with it effectively. Many navigation approaches primarily define success by proximity to the target, often overlooking the necessity for optimal positioning that facilitates subsequent manipulation. To address this, we introduce MoMa-Kitchen, a benchmark dataset comprising over 100k samples that provide training data for models to learn optimal final navigation positions for seamless transition to manipulation. Our dataset includes affordance-grounded floor labels collected from diverse kitchen environments, in which robotic mobile manipulators of different models attempt to grasp target objects amidst clutter. Using a fully automated pipeline, we simulate diverse real-world scenarios and generate affordance labels for optimal manipulation positions. Visual data are collected from RGB-D inputs captured by a first-person view camera mounted on the robotic arm, ensuring consistency in viewpoint during data collection. We also develop a lightweight baseline model, NavAff, for navigation affordance grounding that demonstrates promising performance on the MoMa-Kitchen benchmark. Our approach enables models to learn affordance-based final positioning that accommodates different arm types and platform heights, thereby paving the way for more robust and generalizable integration of navigation and manipulation in embodied AI. Project page: \href{https://momakitchen.github.io/}{https://momakitchen.github.io/}.
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。
