title: Cross Anything: General Quadruped Robot Navigation through Complex
    Terrains
summary: 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/
paper_authors._id: 66a068823011445416f6bdff
paper_authors.name: Shaoting Zhu
paper_authors._id: 66a068823011445416f6be00
paper_authors.name: Derun Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:25:10.862Z
paper_authors._id: 650aa146cc02352e1e412928
paper_authors.avatarUrl: /avatars/207df7719a22c439e8690411ff62c297.svg
paper_authors.fullname: Derun Li
paper_authors.type: user
paper_authors.user: Dylan-LDR
paper_authors._id: 66a068823011445416f6be01
paper_authors.name: Yong Liu
paper_authors._id: 66a068823011445416f6be02
paper_authors.name: Ningyi Xu
paper_authors._id: 66a068823011445416f6be03
paper_authors.name: Hang Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:26:39.546Z
paper_authors._id: 655e583fadafc9cb0d0ef39f
paper_authors.avatarUrl: /avatars/c4c97881208fbeecf9b91d295033a74a.svg
paper_authors.fullname: Hang Zhao
paper_authors.type: user
paper_authors.user: Hangv587
paper_title: Cross Anything: General Quadruped Robot Navigation through Complex
    Terrains
paper_summary: 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/
paper_ai_summary: 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.
paper_ai_keywords: 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