跨越一切：通过复杂地形的普通四足机器人导航

Cross Anything: General Quadruped Robot Navigation through Complex Terrains

July 23, 2024

作者: Shaoting Zhu, Derun Li, Yong Liu, Ningyi Xu, Hang Zhao

cs.AI

摘要

视觉语言模型（VLMs）的应用在各种机器人任务中取得了令人瞩目的成功，但在四足机器人导航中使用基础模型的探索较少。我们介绍了Cross Anything System（CAS），这是一个创新系统，由高级推理模块和低级控制策略组成，使机器人能够穿越复杂的3D地形并到达目标位置。对于高级推理和运动规划，我们提出了一种利用VLM的新颖算法系统，设计了任务分解和闭环子任务执行机制。对于低级运动控制，我们利用概率退火选择（PAS）方法通过强化学习训练控制策略。大量实验证明，我们的整个系统能够准确而稳健地穿越复杂的3D地形，其强大的泛化能力确保了在各种室内和室外场景以及地形中的应用。项目页面：https://cross-anything.github.io/

English

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/