Imagine asking your smart home AI to tidy up the kitchen. Sounds simple enough, but for today’s AI, even basic household tasks present a surprising challenge. Why? Because they struggle with spatial reasoning and temporal understanding – the very skills we humans use effortlessly to navigate our homes. A new research paper introduces "ET-Plan-Bench," a benchmark designed to test how well Large Language Models (LLMs) can handle embodied task planning. This means giving AI a virtual body within simulated environments and seeing if they can follow instructions like “Put the apple in the fridge.” This research is important because it bridges the gap between language comprehension and physical action. The benchmark automatically generates tasks with varying complexity, introducing constraints like spatial relationships ("Put the cup *near* the plate") and temporal dependencies ("First, grab the milk, *then* pour it"). These constraints are crucial for mimicking real-world scenarios. Early tests using leading LLMs like GPT-4 reveal that while AI can handle basic navigation, they stumble when facing challenges like hidden objects or multi-step actions. Interestingly, smaller, open-source models like LLAMA, when fine-tuned with training data, can nearly match GPT-4's performance, underscoring the potential for broader accessibility. The benchmark also explores the impact of "prior knowledge" on task success. Just like knowing your kitchen layout helps you find things faster, AI's ability to complete tasks increases significantly when given spatial priors, such as the location of furniture in a room. This benchmark not only highlights the current limitations of AI but also offers a powerful tool to improve them. It provides a standardized way to evaluate LLMs in embodied tasks, paving the way for more capable and practical AI assistants. While the dream of a fully automated robot butler is still a bit further down the road, this research shows us a clearer path toward making that dream a reality. The more we understand the hurdles AI needs to overcome, the better we can design and train models that can understand, plan, and act in the real world.