AffordDP：具备可迁移可供性的泛化扩散策略
AffordDP: Generalizable Diffusion Policy with Transferable Affordance
AI总结：
针对扩散策略在机器人操作中跨域泛化能力弱的问题，提出融合可迁移可供性的AffordDP方法，通过3D接触信息建模可供性、结合视觉基础模型与点云配准实现迁移，并在扩散采样中加入引导，在仿真与真实场景中均优于现有方法，实现对未见物体实例和类别的泛化操作。
摘要：
基于扩散的策略已在机器人操作任务中展现出优异性能，但在面对域外分布时表现不佳。近期研究尝试通过改进扩散策略的视觉特征编码来提升泛化性，但这类方法的泛化能力通常局限于外观相似的同类别物体。我们的核心洞见是，利用可供性（即定义智能体“在何处”以及“如何”与物体交互的操作先验），能够大幅提升模型对完全未见过的物体实例和类别的泛化能力。我们提出了具备可迁移可供性的扩散策略（AffordDP），旨在实现跨新类别的泛化操作。AffordDP通过3D接触点和接触后轨迹对可供性进行建模，捕捉复杂任务所需的核心静态与动态信息。借助基础视觉模型和点云配准技术估计6D变换矩阵，我们实现了可供性从域内数据到未见物体的迁移。更重要的是，我们在扩散采样过程中引入了可供性引导机制，能够优化动作序列的生成。该引导机制可使生成的动作逐步趋近于针对未见物体的预期操作，同时将生成的动作约束在动作空间的流形内。仿真与真实环境的实验结果表明，AffordDP的性能始终优于此前的基于扩散的方法，能够成功泛化到其他方法无法应对的未见实例和类别。
英文摘要：
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.
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。
