Imagine a robot that can perform tasks it's never seen before, without explicit training. That's the promise of RAM (Retrieval-based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation), a new technique that lets robots learn by watching and remembering. Instead of relying on extensive programming or demonstrations for each new task, RAM allows robots to tap into a vast library of existing knowledge, including videos of humans and other robots interacting with objects. When faced with a new challenge, such as opening a drawer it's never encountered, the robot searches this 'affordance memory' for similar examples. This memory isn't just a collection of videos; it stores information about *how* to interact with objects, like where to grasp and how to move. Once a similar example is found, RAM cleverly transfers this knowledge to the new situation. It analyzes the shape and position of the new object and adapts the actions from the memory accordingly. This whole process happens without any retraining or fine-tuning, making RAM remarkably adaptable. In both simulations and real-world tests, RAM has outperformed existing methods. It has successfully opened drawers, cabinets, and even picked up objects in cluttered environments, all without prior training. What makes RAM even more exciting is its potential. Researchers envision using it to create robots that can learn continuously, adapting to new environments and tasks with minimal human intervention. While RAM isn't perfect (it still struggles with complex tasks like screwing), it represents a significant leap towards more generalizable and adaptable robots.