Researchers propose Spotter, a system where an embodied model leads and executes continuously, while a vision-language model (VLM) monitors and intervenes only when an error is detected, reflects on and corrects it, and returns control. This approach improves performance in embodied model tasks, such as robotics and navigation, by leveraging the strengths of both models.