Learning a universal manipulation policy encompassing doors with diverse categories, geometries and mechanisms, is crucial for future embodied agents to effectively work in complex and broad real-world scenarios. Due to the limited datasets and unrealistic simulation environments, previous works fail to achieve good performance across various doors. In this work, we build a novel door manipulation environment reflecting different realistic door manipulation mechanisms, and further equip this environment with a large-scale door dataset covering 6 door categories with hundreds of door bodies and handles, making up thousands of different door instances. Additionally, to better emulate real-world scenarios, we introduce a mobile robot as the agent and use the partial and occluded point cloud as the observation, which are not considered in previous works while possessing significance for real-world implementations. To learn a universal policy over diverse doors, we propose a novel framework disentangling the whole manipulation process into three stages, and integrating them by training in the reversed order of inference. Extensive experiments validate the effectiveness of our designs and demonstrate our framework's strong performance.

翻译：学习一种涵盖不同类别、几何形状和机构的通用门操作策略，对于未来具身智能体在复杂且广泛的真实场景中高效工作至关重要。由于数据集有限且仿真环境不真实，先前的工作未能实现跨各类门的一致优异性能。在本研究中，我们构建了一个反映不同真实门操作机制的新型门操作环境，并为该环境配备了大规模门数据集，涵盖6种门类别、数百个门体与把手，构成了数千个不同的门实例。此外，为更好地模拟真实场景，我们引入移动机器人作为智能体，采用部分遮挡的点云作为观测信息——这一点在先前工作中未被考虑，但对真实世界部署具有重要意义。为学习跨多样门的通用策略，我们提出了一种新型框架，将整个操作过程解耦为三个阶段，并通过按推理逆序训练的方式将其整合。大量实验验证了设计的有效性，并展示了我们框架的强大性能。