Imagine a robot that can understand your instructions, plan its actions, and execute tasks in the real world, all without explicit programming. That's the promise of Wonderful Team, a new multi-agent framework that leverages the power of Visual Large Language Models (VLLMs) to bring us closer to this vision. Traditional approaches to robotic task planning often involve separate vision and language models, leading to a disconnect between what the robot "sees" and what it "understands." Wonderful Team tackles this challenge by integrating perception, control, and planning within a single VLLM framework. The secret sauce? A team of specialized agents within the VLLM, each handling a specific aspect of the task. The Supervisor agent creates high-level plans, while the Verification agent checks for potential problems like collisions or missing steps. Meanwhile, the Grounding Team works together to pinpoint the exact location of objects, ensuring the robot's actions are precise. This collaborative approach allows Wonderful Team to self-correct and adapt to unexpected situations. For example, if a box needs to be opened, the Verification agent will flag the need to remove the lid first. The Grounding Team then refines the robot's grasp, ensuring it picks up the lid correctly. Researchers tested Wonderful Team on various tasks, both in simulation and with real robots. From placing fruits in color-matched areas to complex maneuvers like drawing a star, the results were impressive. Wonderful Team consistently outperformed traditional methods, especially on tasks that required understanding context and implicit instructions. For instance, given the task "put the banana in the box," Wonderful Team successfully accounted for the need to open the lid, a nuance often missed by other systems. While Wonderful Team shows great promise, some challenges remain. Its 3D reasoning capabilities are still limited, and it sometimes struggles with tasks that require precise height adjustments or understanding partially obscured objects. However, this research is a significant step forward in enabling robots to understand and execute complex tasks directly from human instructions. It also highlights how the rapid improvement of VLLMs is changing the field of robotics, moving us closer to a future where robots are more capable of handling diverse and changing tasks in our everyday lives.