Imagine teaching a robot a complex task, like assembling furniture or cooking a meal. It's not as simple as just showing it once. Traditional imitation learning methods often struggle with long sequences of actions, leading to errors and deviations from the desired outcome. But what if we could give the robot a 'semantic boost'? That's the idea behind SEAL, a new approach that leverages the power of Large Language Models (LLMs) to make imitation learning significantly more effective. LLMs, like those behind ChatGPT, excel at understanding and generating human language. SEAL uses this ability to break down complex tasks into smaller, semantically meaningful sub-goals, like 'find the key,' 'pick it up,' or 'open the door.' This provides a clearer roadmap for the robot to follow. Instead of simply mimicking individual actions, the robot learns to achieve these sub-goals, making it more robust to unforeseen situations. This approach is especially helpful when training data is limited. SEAL uses a clever dual-encoder system. One encoder learns from the LLM-generated sub-goals, while another uses an unsupervised method to map the robot's current state to these sub-goals. This combination ensures the system is both reliable and flexible. Additionally, SEAL emphasizes learning the transitions between sub-goals – those critical moments where the robot switches from one part of the task to the next. Think of it like learning the proper sequence in a recipe. You not only need to know how to chop vegetables and boil water but also when to transition between those steps. This focus on transitions greatly improves the robot's ability to complete complex tasks successfully. Experiments show that SEAL outperforms other imitation learning methods, especially in scenarios with limited training data. It even adapts well to variations in the task, like changing the order of steps in a multi-step process. SEAL represents a significant leap forward in imitation learning. By combining the power of LLMs with a focus on semantic understanding and sub-goal transitions, it opens up new possibilities for teaching robots complex, real-world tasks efficiently. This approach has the potential to revolutionize how we train robots for everything from manufacturing and logistics to household assistance and beyond. The challenges now lie in refining the stability of SEAL during training and extending its capabilities to scenarios where the robot doesn't have complete information about its surroundings. These are exciting avenues for future research, with the promise of even more capable and adaptable robots in the years to come.