Diffusion-based policies have shown impressive performance in robotic manipulation tasks while struggling with out-of-domain distributions. Recent efforts attempted to enhance generalization by improving the visual feature encoding for diffusion policy. However, their generalization is typically limited to the same category with similar appearances. Our key insight is that leveraging affordances--manipulation priors that define "where" and "how" an agent interacts with an object--can substantially enhance generalization to entirely unseen object instances and categories. We introduce the Diffusion Policy with transferable Affordance (AffordDP), designed for generalizable manipulation across novel categories. AffordDP models affordances through 3D contact points and post-contact trajectories, capturing the essential static and dynamic information for complex tasks. The transferable affordance from in-domain data to unseen objects is achieved by estimating a 6D transformation matrix using foundational vision models and point cloud registration techniques. More importantly, we incorporate affordance guidance during diffusion sampling that can refine action sequence generation. This guidance directs the generated action to gradually move towards the desired manipulation for unseen objects while keeping the generated action within the manifold of action space. Experimental results from both simulated and real-world environments demonstrate that AffordDP consistently outperforms previous diffusion-based methods, successfully generalizing to unseen instances and categories where others fail.

翻译：基于扩散的策略在机器人操作任务中展现出卓越性能，但在处理域外分布时仍面临挑战。近期研究尝试通过改进扩散策略的视觉特征编码来增强泛化能力，但其泛化通常局限于具有相似外观的同一类别物体。我们的核心洞见在于：利用功能可供性——即定义智能体“在何处”以及“如何”与物体交互的操作先验——能够显著增强对完全未见过的物体实例及类别的泛化能力。本文提出具有可迁移功能可供性的扩散策略（AffordDP），专为跨新类别物体的通用化操作而设计。AffordDP通过三维接触点及接触后轨迹对功能可供性进行建模，从而捕捉复杂任务所需的静态与动态关键信息。通过利用基础视觉模型与点云配准技术估计6D变换矩阵，实现了从域内数据到未见物体的可迁移功能可供性。更重要的是，我们在扩散采样过程中引入功能可供性引导机制，该机制能够优化动作序列生成。这种引导使生成的动作逐步朝向对未见物体的预期操作方向调整，同时确保生成动作始终位于动作空间的流形之内。仿真环境与真实场景的实验结果表明，AffordDP在以往扩散方法失效的未见实例与类别上均能成功泛化，且性能持续优于现有基于扩散的方法。