Object rearrangement, a fundamental challenge in robotics, demands versatile strategies to handle diverse objects, configurations, and functional needs. To achieve this, the AI robot needs to learn functional rearrangement priors in order to specify precise goals that meet the functional requirements. Previous methods typically learn such priors from either laborious human annotations or manually designed heuristics, which limits scalability and generalization. In this work, we propose a novel approach that leverages large models to distill functional rearrangement priors. Specifically, our approach collects diverse arrangement examples using both LLMs and VLMs and then distills the examples into a diffusion model. During test time, the learned diffusion model is conditioned on the initial configuration and guides the positioning of objects to meet functional requirements. In this manner, we create a handshaking point that combines the strengths of conditional generative models and large models. Extensive experiments on multiple domains, including real-world scenarios, demonstrate the effectiveness of our approach in generating compatible goals for object rearrangement tasks, significantly outperforming baseline methods.

翻译：物体重排是机器人领域的一项基础挑战，需要灵活的策略来处理多样化的物体、配置和功能需求。为实现这一目标，AI机器人需学习功能重排先验，以指定满足功能要求的精确目标。以往方法通常通过繁琐的人工标注或手动设计的启发式策略学习此类先验，这限制了可扩展性和泛化能力。本文提出一种新颖方法，利用大型模型蒸馏功能重排先验。具体而言，我们的方法同时使用大语言模型（LLM）和视觉语言模型（VLM）收集多样化的排列示例，并将这些示例蒸馏至扩散模型中。在测试阶段，学习到的扩散模型以初始配置为条件，指导物体定位以满足功能需求。通过这种方式，我们构建了一个结合条件生成模型与大型模型优势的衔接点。在包括真实场景在内的多个领域进行的广泛实验表明，我们的方法在生成物体重排任务的兼容目标方面显著优于基线方法。