无需训练和模拟，双机器人协同导航实现高效避障
Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems
- 人形机器人协调+四足机器人探索，通过多模态大模型引导路径
- 在复杂环境中路径成功率超90%，性能接近人类水平
- 适合真实场景部署，尤其适合缺乏先验知识的异构机器人系统
我们提出三零路径规划（TZPP），一种无需训练、无需先验知识、无需仿真的异构多机器人协同框架。TZPP采用协调者-探索者架构：人形机器人负责任务协调，四足机器人在多模态大语言模型指导下探索并识别可行路径。我们在Unitree G1和Go2机器人上实现并评估了该方法，覆盖多种室内外环境，包括障碍物密集与地标稀疏场景。实验表明，TZPP在未见过的场景中表现出鲁棒性与强适应能力，路径规划效率达到人类可比水平。通过消除对训练和仿真依赖，TZPP为异构机器人协作的真实世界应用提供了可行路径。代码与视频见：https://github.com/triple-zeropp/Triple-zero-robot-agent
原文摘要 · Abstract (English)
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
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