物理的接地を伴う視覚言語モデルによる屋外ロボットナビゲーション
Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
視覚言語モデルに固有受容感覚データを組み合わせて地形の変形性や滑りやすさを評価し、屋外での走行可能領域を動的に推定するナビゲーション手法を提案。脚式・車輪式ロボットで成功率を最大50%向上させた。
著者: Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne, Ruiqi Xian, Tianrui Guan, Mohamed Khalid M Jaffar, Vignesh Rajagopal, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical grounding that is used to assess intrinsic terrain properties such as deformability and slipperiness. We use proprioceptive-based sensing, which provides direct measurements of these physical properties, and enhances the overall semantic understanding of the terrains. Our formulation uses in-context learning to ground the VLM's semantic understanding with proprioceptive data to allow dynamic updates of traversability estimates based on the robot's real-time physical interactions with the environment. We use the updated traversability estimations to inform both the local and global planners for real-time trajectory replanning. We validate our method on a legged robot (Ghost Vision 60) and a wheeled robot (Clearpath Husky), in diverse real-world outdoor environments with different deformable and slippery terrains. In practice, we observe significant improvements over state-of-the-art methods by up to 50% increase in navigation success rate.
