  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. Code, data and videos are avaible on
https://unidoormanip.github.io/.
