SARO: 視覚言語モデルによる3D地形を横断する空間認識ロボットシステム
SARO: Space-Aware Robot System for Terrain Crossing via Vision-Language Model
視覚言語モデルを高レベル推論に用い、タスク分解と閉ループ実行、強化学習による低レベル制御を組み合わせて、四足ロボットが3D地形を横断して目標地点に到達できるシステムを構築した。
著者: Shaoting Zhu, Derun Li, Linzhan Mou, Yong Liu, Ningyi Xu, Hang Zhao
分類: cs.RO
原文アブストラクト
The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks. However, there are few explorations for these foundation models used in quadruped robot navigation through terrains in 3D environments. In this work, we introduce SARO (Space Aware Robot System for Terrain Crossing), an innovative system composed of a high-level reasoning module, a closed-loop sub-task execution module, and a low-level control policy. It enables the robot to navigate across 3D terrains and reach the goal position. For high-level reasoning and execution, 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 effectively train a control policy by reinforcement learning. Numerous experiments show that our whole system can accurately and robustly navigate across several 3D terrains, and its generalization ability ensures the applications in diverse indoor and outdoor scenarios and terrains. Project page: https://saro-vlm.github.io/
