UniETP: Unifying Environments for Generalizable Embodied Task Planning

This paper focuses on the problem of Embodied Task Planning, where an agent is required to execute a sequence of atomic actions within an interactive environment to complete a user-specified task. Though a variety of simulators and datasets have previously been built for this task, these efforts are largely isolated, with each using its own observation format, action type, and task domain. This fragmentation complicates comprehensive model evaluation and hinders the scalability of training data. As an effort towards generalizable embodied planning, we propose UniETP, a unified interface integrating four commonly used simulators (AI2-THOR, VirtualHome, Habitat, BEHAVIOR). UniETP is characterized by both standardization and diversity. On one hand, it formalizes all the simulators into a consistent observation and action space, and builds an evaluation system to support complicated task goals. On the other hand, it enhances task diversity and complexity across dimensions such as task logic, instance grounding, and instruction understanding, constructing a new dataset with varied levels of difficulty automatically. Extensive experiments on the proposed benchmark are conducted to evaluate the embodied planning capabilities of recent models and validate the utility of our framework.