Imagine asking your robot to fetch you a snack, only to have it return with a cleaning spray because they’re both stored under the sink. Robots often struggle with the kind of common sense reasoning humans take for granted. New research is tackling this challenge by combining the power of large language models (LLMs), like those behind ChatGPT, with a robot’s existing knowledge of its environment. LLMs excel at understanding general knowledge and nuanced language, but they can also “hallucinate” – generating incorrect or nonsensical information. This poses a safety risk in robotics, where inaccurate actions could have real-world consequences. The researchers address this by cross-referencing the LLM's suggestions against the robot’s internal database of known objects and locations, essentially fact-checking the AI's ideas. This two-pronged approach helps robots understand vague requests like “find a fruit” by first consulting their knowledge base and, if that fails, querying the LLM for likely locations. The LLM, primed with details of the robot’s specific environment, suggests places like the dining table or kitchen counter. This combination significantly reduces the need for constant user clarification, making robot interactions smoother and more efficient. Early tests in simulated home environments show promising results. Robots equipped with this combined approach successfully complete tasks like fetching specific items, even with incomplete instructions, while minimizing unnecessary back-and-forth with users. However, the system isn’t perfect. Object recognition errors and the occasional LLM hallucination still cause hiccups. Future research will focus on improving the robustness of these systems, expanding the range of tasks they can handle, and ultimately bringing this technology from simulation into our real homes. This research takes an important step toward building truly helpful robots that can understand our needs, even when we don't perfectly articulate them.