# Language-Augmented Symbolic Planner for Open-World Task Planning

arXiv: https://arxiv.org/abs/2407.09792 · HTML: https://mlsanity.com/paper/2407.09792/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2024.

## Built on by (most cited first, 8 of 8)

- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation](https://mlsanity.com/paper/2412.05893/) (2024)
- [NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains](https://mlsanity.com/paper/2503.00870/) (2025)
- [The State of Robot Motion Generation](https://mlsanity.com/paper/2410.12172/) (2024)
- [CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building](https://mlsanity.com/paper/2410.13756/) (2024)
- [PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models](https://mlsanity.com/paper/2506.20097/) (2025)
- [REFLEX: Metacognitive Reasoning for Reflective Zero-Shot Robotic Planning with Large Language Models](https://mlsanity.com/paper/2505.14899/) (2025)
- [Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents](https://mlsanity.com/paper/2604.27699/) (2026)

## Abstract (arXiv)

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.