Cross Anything: General Quadruped Robot Navigation through Complex
  Terrains
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/
66a068823011445416f6bdff
Shaoting Zhu
66a068823011445416f6be00
Derun Li
admin_assigned
2024-07-24T08:25:10.862Z
650aa146cc02352e1e412928
/avatars/207df7719a22c439e8690411ff62c297.svg
Derun Li
user
Dylan-LDR
66a068823011445416f6be01
Yong Liu
66a068823011445416f6be02
Ningyi Xu
66a068823011445416f6be03
Hang Zhao
admin_assigned
2024-07-24T08:26:39.546Z
655e583fadafc9cb0d0ef39f
/avatars/c4c97881208fbeecf9b91d295033a74a.svg
Hang Zhao
user
Hangv587
Cross Anything: General Quadruped Robot Navigation through Complex
  Terrains
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/
Cross Anything System (CAS) integrates vision-language models and probability annealing selection for high-level reasoning and low-level control, enabling robust quadruped robot navigation across complex terrains.
vision-language models quadruped robot navigation high-level reasoning motion planning task decomposition closed-loop sub-task execution low-level locomotion control Probability Annealing Selection reinforcement learning