Imagine asking a robot to "grab something for my child to play with" in a messy room. This seemingly simple request involves a complex chain of reasoning: understanding what a child might play with, identifying suitable objects amidst clutter, planning how to reach them, and carefully executing a grasp to avoid knocking things over. This is the challenge of "open-world grasping," and new research is making impressive strides using large vision-language models (LVLMs). Traditional robots struggle in these unstructured scenarios because they need pre-programmed object knowledge. LVLMs, however, possess a wealth of semantic understanding from their massive text and image training. This research introduces a system called OWG (Open World Grasper) that effectively combines LVLMs with more traditional tools. The OWG pipeline works in three stages. First, it uses a cutting-edge segmentation model and visual markers to help the LVLM locate and ground the target object referred to in the user's request, even in cluttered environments. Next, OWG cleverly plans the grasp. If the target object is blocked, it determines which obstructing objects need removing first. Finally, the system generates a range of possible grasps and uses the LVLM to rank them based on contact points and the shapes of surrounding objects, selecting the safest and most efficient maneuver. Tests show OWG significantly outperforms existing methods in both simulated and real-world robotic grasping trials. It’s particularly effective with user requests involving categories, attributes, or relations between objects—the types of instructions we naturally give. While the system still relies on underlying segmentation and grasping models, which can introduce errors, it presents a promising step towards truly robust, open-world robotic manipulation. This research leverages the combined power of advanced computer vision techniques with the semantic richness of LLMs, moving us closer to a future where robots seamlessly navigate and interact with our complex, everyday world.