  Enabling robotic agents to perform complex long-horizon tasks has been a
long-standing goal in robotics and artificial intelligence (AI). Despite the
potential shown by large language models (LLMs), their planning capabilities
remain limited to short-horizon tasks and they are unable to replace the
symbolic planning approach. Symbolic planners, on the other hand, may encounter
execution errors due to their common assumption of complete domain knowledge
which is hard to manually prepare for an open-world setting. In this paper, we
introduce a Language-Augmented Symbolic Planner (LASP) that integrates
pre-trained LLMs to enable conventional symbolic planners to operate in an
open-world environment where only incomplete knowledge of action preconditions,
objects, and properties is initially available. In case of execution errors,
LASP can utilize the LLM to diagnose the cause of the error based on the
observation and interact with the environment to incrementally build up its
knowledge base necessary for accomplishing the given tasks. Experiments
demonstrate that LASP is proficient in solving planning problems in the
open-world setting, performing well even in situations where there are multiple
gaps in the knowledge.
