Recent advancements in large language models (LLMs) are enabling the development of versatile robot-use agents that can perform complex tasks through improved coding abilities, diverse training data, and in-context learning without extensive retraining. Researchers anticipate that within the next two years, these general-purpose robots will understand natural language instructions and operate at human-teenager levels, driven by ongoing innovations in model efficiency, skill consolidation, and multi-modal data integration.