AI 中文总结
研究针对移动操作中FloAff预测难题，提出含规范表示学习和渐进式可供性先验学习的统一框架，引入CFAR和PFAL，建立FloAff-Kitchen基准，实验证明该方法优于基线，验证了组件贡献。
AI 中文摘要
移动操作要求机器人识别能使下游操作成功最大化的地面可供性（FloAff），而非仅确保导航可行性。FloAff预测是目标条件下的局部空间推理问题，但现有方法存在因无关空间上下文和任意物体方向导致的表示模糊问题，且在不同操作技能中纠缠共享和特定任务知识。为应对这些挑战，我们提出一个从自我中心多模态感知进行FloAff预测的统一框架，包括规范表示学习和渐进式可供性先验学习。具体而言，我们引入规范地面可供性表示（CFAR），通过保留与可供性相关的局部结构同时消除与机器人基座放置无关的干扰空间变化来学习规范交互几何。我们还提出渐进式地面可供性学习（PFAL），从基础操作任务学习可转移的FloAff先验并将其逐步适应不同下游操作技能。为便于系统评估，我们建立了首个涵盖不同操作技能、场景布局、家具风格和视角的跨场景、多视图FloAff-Kitchen基准。在三种基准设置上的大量实验表明我们的方法持续优于强大基线，同时消融研究验证了每个提出组件的贡献。
英文摘要
Mobile manipulation requires robots to identify Floor Affordance (FloAff) that maximizes downstream manipulation success rather than merely ensuring navigation feasibility. FloAff prediction is a target-conditioned local spatial reasoning problem, yet existing methods suffer from representation ambiguity caused by irrelevant spatial context and arbitrary object orientations, while entangling shared and task-specific knowledge across heterogeneous manipulation skills. To address these challenges, we propose a unified framework for FloAff prediction from egocentric multimodal perception, consisting of canonical representation learning and progressive affordance prior learning. Specifically, we introduce a Canonical Floor Affordance Representation (CFAR), which learns canonical interaction geometry by preserving affordance-relevant local structure while eliminating nuisance spatial variations unrelated to robot base placement. We further propose Progressive Floor Affordance Learning (PFAL), which learns transferable FloAff priors from a foundation manipulation task and progressively adapts them to heterogeneous downstream manipulation skills. To facilitate systematic evaluation, we establish the first cross-scene, multi-view FloAff-Kitchen benchmark covering diverse manipulation skills, scene layouts, furniture styles, and viewpoints. Extensive experiments on three benchmark settings demonstrate that our method consistently outperforms strong baselines, while ablation studies validate the contribution of each proposed component. Project page: https://csu-hero-lab.github.io/FloAff-Kitchen_Web/
