  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/
