UniDoorManip: Learning Universal Door Manipulation Policy Over Large-Scale and Diverse Door Manipulation Environments

Abstract

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 studies 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. To learn a universal policy over diverse doors, we propose a novel framework disentangling the whole manipulation process into three stages, and integrating them through conditional training. Extensive experiments validate the effectiveness of our designs and demonstrate our framework's strong performance in simulation and real world.