Imagine a world where robots seamlessly understand our instructions, navigating complex environments with ease. Researchers are tackling this challenge by teaching robots to interpret natural language references to 3D objects, a task surprisingly difficult for AI. A new approach called Transcrib3D combines the power of 3D object detection with the reasoning capabilities of large language models (LLMs). Instead of trying to create a shared representation between language and 3D data, which requires tons of annotated data, Transcrib3D uses text as a bridge. It first detects objects in a 3D scene, then translates the object's properties (like shape, location, and color) into text, creating a "scene transcript." An LLM then processes this transcript along with the user's instructions, using a clever iterative process to reason and refine its understanding. This allows the robot to understand complex instructions like "Pick up the small red block on top of the blue cube." The results are impressive, with Transcrib3D achieving state-of-the-art performance on 3D object localization benchmarks. Real-world robot experiments further demonstrate its effectiveness, enabling robots to perform pick-and-place tasks with complex instructions. While challenges remain, such as improving the accuracy of 3D object detection and handling finer details, Transcrib3D represents a significant step towards more intuitive and effective human-robot interaction. This technology has the potential to revolutionize various fields, from manufacturing and logistics to healthcare and home assistance, paving the way for robots that truly understand our world.