Imagine an AI-powered robot chef effortlessly navigating a kitchen, whipping up culinary masterpieces without explicit instructions for every single step. That's the tantalizing vision behind new research on open-world task planning. Traditional symbolic planners, the brains behind such robots, stumble when faced with real-world uncertainties. They rely on rigid, pre-programmed knowledge, much like a recipe that doesn't account for ingredient substitutions or unexpected spills. What if the milk spills while pouring it into a glass? What if the fridge is closed when it's time to get an ingredient? This research introduces LASP, a Language-Augmented Symbolic Planner, that bridges this knowledge gap by adding the adaptability and common sense of large language models (LLMs) to the precision of symbolic planners. Like a human chef adjusting to a missing ingredient, LASP learns from its mistakes. When an error occurs, LASP analyzes the situation using an LLM, much like an experienced chef diagnosing a culinary mishap. It then refines its understanding of the environment and actions to prevent similar mistakes. For instance, if the robot spills milk while trying to pour it onto a sandwich, LASP recognizes that pouring milk onto a non-container is problematic. It then updates its knowledge base with the crucial precondition that liquids should only be poured into containers. The next time the robot tries to make a sandwich, it won’t try to pour milk onto it! LASP also tackles the problem of incomplete knowledge. If the robot needs a microwave-safe container but doesn't know which objects have this property, LASP leverages the LLM to predict suitable objects and update the robot’s knowledge base. While still in its experimental phase, LASP shines in kitchen-based planning tasks like serving fruit, storing food, and preparing simple meals. The results show remarkable improvements over existing LLM-based planning methods, particularly in complex, multi-step scenarios. The combination of symbolic reasoning and language understanding could revolutionize how robots operate in unpredictable environments. Challenges remain, like improving the LLM's ability to read PDDL expressions, the language of symbolic planners. Future research aims to make LASP more robust to noisy real-world data and to enhance its ability to learn without explicit errors. This research lays the foundation for truly intelligent robots that can learn, adapt, and perform tasks in the real world as effectively as humans do.