This paper presents Triple Zero Path Planning (TZPP), a collaborative navigation framework for heterogeneous multi-robot systems achieving zero training, zero prior knowledge, and zero simulation requirements. The system employs a coordinator-explorer architecture where a Unitree G1 humanoid robot performs task coordination while a Unitree Go2 quadruped robot explores and identifies feasible paths using guidance from a multimodal large language model. The framework is evaluated across diverse indoor and outdoor environments including obstacle-rich and landmark-sparse settings, demonstrating robust and human-comparable navigation efficiency. By eliminating reliance on traditional training and simulation pipelines, TZPP provides a practical approach for real-world deployment of heterogeneous robot cooperation with strong adaptability to unseen scenarios.