Imagine asking your robot to grab your coffee mug. Simple, right? But what if the robot can't distinguish your favorite mug from other cups? This highlights a critical challenge in human-robot interaction: aligning human and robot perception. Researchers are tackling this with SYNERGAI, a system that helps robots understand our world more like we do. SYNERGAI uses a 3D Scene Graph (3DSG) to represent the environment, encoding objects, their relationships, and even personalized details like "my coffee mug." This graph acts as a bridge between human language and robot perception. When you give a command, SYNERGAI uses a large language model (LLM) to break it down into smaller steps. It then utilizes specialized tools to gather information from the 3DSG, making sure it understands exactly what you're asking for. But what happens when the robot gets it wrong? SYNERGAI includes a unique feedback mechanism that allows you to correct its understanding naturally, either through language or by directly interacting with a 3D visualization of the scene. This way, you can teach your robot what "coffee mug" really means to you. Initial tests of SYNERGAI are promising. In experiments involving real-world scenarios, it successfully aligned with human instructions a significant portion of the time. Even more exciting, the knowledge gained during these interactions transfers to new tasks, meaning the robot genuinely learns from its mistakes. While challenges remain, SYNERGAI represents an exciting step towards seamless human-robot collaboration, where robots not only execute our commands but truly understand our intentions.