We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi-robot systems that requires zero training, zero prior knowledge, and zero simulation. TZPP employs a coordinator--explorer architecture: a humanoid robot handles task coordination, while a quadruped robot explores and identifies feasible paths using guidance from a multimodal large language model. We implement TZPP on Unitree G1 and Go2 robots and evaluate it across diverse indoor and outdoor environments, including obstacle-rich and landmark-sparse settings. Experiments show that TZPP achieves robust, human-comparable efficiency and strong adaptability to unseen scenarios. By eliminating reliance on training and simulation, TZPP offers a practical path toward real-world deployment of heterogeneous robot cooperation. Our code and video are provided at: https://github.com/triple-zeropp/Triple-zero-robot-agent

翻译：我们提出三重零路径规划（TZPP），一种面向异构多机器人系统的协作框架，该框架无需训练、无需先验知识、无需仿真。TZPP采用协调器-探索者架构：人形机器人负责任务协调，四足机器人则借助多模态大语言模型的引导，探索并识别可行路径。我们在宇树G1与Go2机器人上实现了TZPP，并在多样化室内外环境中进行了评估，包括障碍密集及地标稀疏场景。实验表明，TZPP能够达到稳健的、与人类相媲美的效率，并对未见场景展现出强大的适应能力。通过消除对训练和仿真的依赖，TZPP为异构机器人协作的实际部署提供了一条可行路径。我们的代码与视频见：https://github.com/triple-zeropp/Triple-zero-robot-agent