UAD：用于机器人操作泛化的无监督可用性蒸馏
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
AI总结：
针对机器人操作中现有视觉可用性预测依赖人工标注或预定义任务的问题，提出无监督可用性蒸馏方法UAD，利用基础模型自动生成标注数据训练模型，提升对野外场景、未知物体及指令的泛化能力。
摘要：
在开放式任务指令下，理解细粒度的物体可用性对于机器人在非结构化环境中操作物体至关重要。然而，现有的视觉可用性预测方法通常依赖手动标注数据，或仅基于预定义的任务集。我们提出UAD（无监督可用性蒸馏），这是一种无需任何手动标注即可将基础模型中的可用性知识蒸馏到任务条件可用性模型的方法。通过利用大型视觉模型和视觉语言模型的互补优势，UAD自动为大规模数据集标注详细的<指令、视觉可用性>对。尽管仅在模拟中的渲染物体上训练，UAD仅在冻结特征之上训练轻量级任务条件解码器，却对野外机器人场景和各种人类活动表现出显著的泛化能力。使用UAD提供的可用性作为观察空间，我们展示了一种模仿学习策略，在仅训练10次演示后，对未见过的物体实例、物体类别甚至任务指令的变化表现出良好的泛化能力。项目网站：https://unsup-affordance.github.io/
英文摘要：
Understanding fine-grained object affordances is imperative for robots to manipulate objects in unstructured environments given open-ended task instructions. However, existing methods of visual affordance predictions often rely on manually annotated data or conditions only on a predefined set of tasks. We introduce UAD (Unsupervised Affordance Distillation), a method for distilling affordance knowledge from foundation models into a task-conditioned affordance model without any manual annotations. By leveraging the complementary strengths of large vision models and vision-language models, UAD automatically annotates a large-scale dataset with detailed $<$instruction, visual affordance$>$ pairs. Training only a lightweight task-conditioned decoder atop frozen features, UAD exhibits notable generalization to in-the-wild robotic scenes and to various human activities, despite only being trained on rendered objects in simulation. Using affordance provided by UAD as the observation space, we show an imitation learning policy that demonstrates promising generalization to unseen object instances, object categories, and even variations in task instructions after training on as few as 10 demonstrations. Project website: https://unsup-affordance.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。
