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Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems

Yaxuan Wang, Yifan Xiang, Ke Li, Xun Zhang, BoWen Ye, Zhuochen Fan, Fei Wei, Tong Yang

Abstract

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

Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems

Abstract

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
Paper Structure (17 sections, 3 equations, 2 figures, 9 tables, 1 algorithm)

This paper contains 17 sections, 3 equations, 2 figures, 9 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. Collaboration of Heterogeneous Agents
  4. Integration of VLMs and Robotics
  5. Our TZPP System
  6. Overview
  7. Humanoid Pipeline
  8. Quadruped Pipeline
  9. Adaptive Mode X/Y Strategy
  10. EXPERIMENT
  11. Experimental Setup
  12. Comparative Evaluation of TZPP and Human Operators in Collaborative Path Planning
  13. Ablation Study on Mode X/Y Mechanism
  14. In Open Scenarios Lacking Landmarks
  15. In Scenarios without Direct Accessible Paths
  16. ...and 2 more sections

Figures (2)

  • Figure 1: System pipeline of the proposed multi-agent collaboration framework. Given observations from both the humanoid and quadruped perspectives, the system performs reasoning to determine whether the target is directly reachable. If the destination is visible, the humanoid plans and executes obstacle-aware navigation. Otherwise, the humanoid delegates exploration to the quadruped, which surveys the environment and provides feedback on path feasibility. Based on this feedback, the humanoid updates its plan and continues navigation. This iterative collaboration enables robust decision-making and efficient task completion in complex real-world scenarios.
  • Figure 2: Iterative decision-making logic of the humanoid agent. The humanoid evaluates path conditions and adaptively determines whether to proceed directly or request assistance from the quadruped. When path conditions are unfavorable, the humanoid iteratively leverages quadruped-guided exploration to identify beneficial waypoints, refining its navigation plan through feedback. Once a feasible waypoint is established, the humanoid proceeds toward it. In contrast, when path conditions are favorable, the humanoid directly selects a waypoint and navigates to the target without additional exploration. This iterative strategy enables robust and efficient navigation under varying environmental conditions.