Imagine teaching a robot a new task, not with complex code or lengthy verbal instructions, but simply by guiding its arm. Researchers at the University of Pennsylvania's GRASP Laboratory are making this a reality with a groundbreaking approach to human-robot collaboration. Their system leverages the power of large language models (LLMs) to enable robots to understand and anticipate human actions in complex tasks, like cooking. The robot proactively tries to help, interpreting the scene and attempting appropriate actions. But what happens when the robot makes a mistake? Instead of yelling complex commands, you simply nudge the robot's arm in the right direction – a physical correction that instantly clarifies your intent. This innovative method, dubbed “physical correction as the collaborative interface,” allows for seamless, real-time feedback. The robot’s internal system uses a probabilistic model to estimate its actions and integrates physical corrections as valuable new data. This feedback loop isn't just reactive; it’s a learning experience. The robot translates these physical cues back into language, feeding them back to the LLM to improve future performance. The research demonstrates this in a simulated cooking scenario, where the robot learns to place a pot on the stove after being physically guided. This approach offers exciting potential for intuitive robot training in various settings. While current research uses a simulated environment, future work will focus on real-world object manipulation and advanced integration with LLM learning. This research takes a significant step towards a future where robots can learn from us as effortlessly as we learn from each other, blurring the lines between human instruction and robotic action.