The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks, but there are few explorations for foundation models used in quadruped robot navigation. We introduce Cross Anything System (CAS), an innovative system composed of a high-level reasoning module and a low-level control policy, enabling the robot to navigate across complex 3D terrains and reach the goal position. For high-level reasoning and motion planning, we propose a novel algorithmic system taking advantage of a VLM, with a design of task decomposition and a closed-loop sub-task execution mechanism. For low-level locomotion control, we utilize the Probability Annealing Selection (PAS) method to train a control policy by reinforcement learning. Numerous experiments show that our whole system can accurately and robustly navigate across complex 3D terrains, and its strong generalization ability ensures the applications in diverse indoor and outdoor scenarios and terrains. Project page: https://cross-anything.github.io/

翻译：视觉语言模型（VLM）在各类机器人任务中已取得显著成功，但基础模型在四足机器人导航领域的应用探索尚少。本文提出跨万物系统（CAS），这是一个由高层推理模块与底层控制策略构成的创新系统，使机器人能够在复杂三维地形中导航并抵达目标位置。针对高层推理与运动规划，我们提出一种新颖的算法系统，该系统利用视觉语言模型，设计了任务分解机制与闭环子任务执行框架。在底层运动控制方面，我们采用概率退火选择（PAS）方法，通过强化学习训练控制策略。大量实验表明，我们的完整系统能够精准、鲁棒地在复杂三维地形中实现导航，其强大的泛化能力确保了系统在多样化的室内外场景与地形中的适用性。项目主页：https://cross-anything.github.io/