Imagine teaching a robot a new task without explicitly programming it or showing it countless examples. This seemingly futuristic scenario is becoming a reality thanks to the power of Large Language Models (LLMs). Researchers are now exploring how LLMs can empower robots to perform complex manipulations with zero prior training, opening doors to a new era of adaptable and versatile robotic systems. A significant hurdle in traditional robotic manipulation is the need for extensive training data and specific programming for each task. This makes robots rigid and unable to adapt to new situations. However, LLMs, with their vast knowledge base and impressive reasoning abilities, offer a potential solution. They can translate high-level instructions, like 'stack these blocks,' into a sequence of actions the robot can understand and execute. But simply generating a long list of instructions isn't enough. LLMs, like their human counterparts, can sometimes make mistakes, especially when dealing with complex, multi-step tasks. These 'hallucinations,' where the LLM generates nonsensical or incorrect instructions, can lead to errors in the robot's actions. Moreover, the real world is unpredictable. A robot might drop an object, encounter an unexpected obstacle, or the environment might change mid-task. A pre-planned sequence of actions can’t handle such dynamic situations. To address these challenges, researchers have developed MALMM, a Multi-Agent Large Language Model for Manipulation. This innovative framework uses a team of specialized LLM agents working together. There's a Planner agent that devises the high-level strategy, a Coder agent that translates the plan into specific robot commands, and a Supervisor agent that oversees the entire operation, managing communication and ensuring everything runs smoothly. This collaborative approach makes the system more robust and less prone to hallucinations. Crucially, MALMM incorporates real-time feedback from the robot's environment. After each step, the system observes the outcome and adjusts the plan if necessary. This allows the robot to recover from mistakes, adapt to changes, and successfully complete tasks even in dynamic environments. In experiments, MALMM outperformed existing zero-shot robotic manipulation methods, particularly in longer, more complex tasks. While still in its early stages, this research demonstrates the exciting potential of LLMs to revolutionize robotics. Imagine robots capable of understanding natural language instructions, learning new tasks quickly, and adapting to unforeseen circumstances – all without extensive programming. This technology could transform industries from manufacturing and logistics to healthcare and even household tasks, paving the way for a future where robots are truly intelligent and versatile partners.