  The emergence of large language models (LLMs) has increasingly drawn
attention to the use of LLMs for human-like planning. Existing work on
LLM-based planning either focuses on leveraging the inherent language
generation capabilities of LLMs to produce free-style plans, or employs
reinforcement learning approaches to learn decision-making for a limited set of
actions within restricted environments. However, both approaches exhibit
significant discrepancies from the open and executable requirements in
real-world planning. In this paper, we propose a new planning task--open
grounded planning. The primary objective of open grounded planning is to ask
the model to generate an executable plan based on a variable action set,
thereby ensuring the executability of the produced plan. To this end, we
establishes a benchmark for open grounded planning spanning a wide range of
domains. Then we test current state-of-the-art LLMs along with five planning
approaches, revealing that existing LLMs and methods still struggle to address
the challenges posed by grounded planning in open domains. The outcomes of this
paper define and establish a foundational dataset for open grounded planning,
and shed light on the potential challenges and future directions of LLM-based
planning.
