  Language models (LMs) have demonstrated their capability in possessing
commonsense knowledge of the physical world, a crucial aspect of performing
tasks in everyday life. However, it remains unclear **whether LMs have the
capacity to generate grounded, executable plans for embodied tasks.** This is a
challenging task as LMs lack the ability to perceive the environment through
vision and feedback from the physical environment. In this paper, we address
this important research question and present the first investigation into the
topic. Our novel problem formulation, named **G-PlanET**, inputs a high-level
goal and a data table about objects in a specific environment, and then outputs
a step-by-step actionable plan for a robotic agent to follow. To facilitate the
study, we establish an **evaluation protocol** and design a dedicated metric to
assess the quality of the plans. Our experiments demonstrate that the use of
tables for encoding the environment and an iterative decoding strategy can
significantly enhance the LMs' ability in grounded planning. Our analysis also
reveals interesting and non-trivial findings.
