Imagine a robot smoothly navigating a crowded hospital, effortlessly yielding to hurried doctors and anxious patients. This isn't science fiction, but the promise of a new system called SocRATES. Researchers are tackling the complex challenge of teaching robots to be socially aware as they move through human environments. Current methods often focus on basic metrics like avoiding collisions, but true social navigation requires a deeper understanding of human behavior. SocRATES uses the power of large language models (LLMs) and computer vision to automatically create realistic simulations of social situations. These scenarios range from simple hallway interactions to complex multi-person encounters, like navigating a crowded elevator. Instead of painstakingly hand-coding each scenario, researchers can simply provide high-level descriptions, and SocRATES fills in the details, generating lifelike pedestrian movements and robot paths. This not only speeds up testing, but also allows for a much wider range of scenarios to be explored, helping to identify and address potential social awkwardness in robots. Early tests show that SocRATES can produce believable simulations in under a minute, drastically reducing the time and cost involved in training socially intelligent robots. While still in its early stages, SocRATES promises to revolutionize how we develop and evaluate social navigation algorithms, paving the way for robots that seamlessly integrate into our daily lives.