Imagine a robot effortlessly navigating your kitchen, preparing a meal without explicit instructions for every step. This seemingly futuristic scenario is closer than you think, thanks to innovative research in combining the power of Large Language Models (LLMs) with classical planning algorithms. Researchers have developed InterPreT, a groundbreaking framework that allows robots to learn abstract concepts and plan complex tasks by receiving feedback in natural language. This approach bridges the gap between human intuition and robotic execution, enabling robots to understand and generalize tasks in a way never before possible. Traditionally, robots have relied on meticulously pre-programmed instructions. This rigid approach struggles when faced with novel situations or complex, multi-step goals. InterPreT tackles this challenge by allowing robots to learn 'predicates,' which are essentially symbolic representations of real-world concepts. For example, a predicate like 'on_table(apple)' allows the robot to understand the relationship between the apple and the table. These predicates are learned through interactive feedback from human users. Instead of requiring complex code, users can simply tell the robot when it has achieved a goal or why a proposed action is infeasible. This feedback is then used by an LLM, like GPT-4, to refine the robot's understanding of these predicates, effectively teaching it to 'think' in a more human-like way. The real magic happens when these learned predicates are combined with 'operators,' which describe the effects of actions on the environment. For instance, the operator associated with 'pick_up(apple)' would describe how this action changes the state of the apple from 'on_table' to 'in_gripper.' Together, predicates and operators form a planning domain, allowing the robot to reason about how to achieve complex goals. In both simulated and real-world tests, InterPreT has shown remarkable results. Robots using this framework can successfully complete complex tasks, even those involving novel objects or goals they haven't encountered before. This generalizability is a key step towards creating truly versatile and adaptable robots. While the results are promising, challenges remain. The current system assumes that the world can be perfectly modeled symbolically, which isn't always the case. Future research aims to address this by incorporating continuous world interactions and handling uncertainty in action outcomes. Despite these limitations, InterPreT represents a significant leap forward in robotics. By leveraging the power of language and human interaction, we are not just programming robots; we are teaching them to learn, reason, and plan, paving the way for a future where robots can seamlessly integrate into our everyday lives.