全方向カメラと複数の視覚言語モデルによる事前知識不要の反射型オープンボキャブラリナビゲーション
Reflex-Based Open-Vocabulary Navigation without Prior Knowledge Using Omnidirectional Camera and Multiple Vision-Language Models
全方向カメラと事前学習済み視覚言語モデルを組み合わせ、地図構築や学習なしで反射的に目標物体へ移動するロボットナビゲーション手法を提案し、Fetchロボットで検証した。
著者: Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Naoto Tsukamoto, Kei Okada, Masayuki Inaba
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Various robot navigation methods have been developed, but they are mainly based on Simultaneous Localization and Mapping (SLAM), reinforcement learning, etc., which require prior map construction or learning. In this study, we consider the simplest method that does not require any map construction or learning, and execute open-vocabulary navigation of robots without any prior knowledge to do this. We applied an omnidirectional camera and pre-trained vision-language models to the robot. The omnidirectional camera provides a uniform view of the surroundings, thus eliminating the need for complicated exploratory behaviors including trajectory generation. By applying multiple pre-trained vision-language models to this omnidirectional image and incorporating reflective behaviors, we show that navigation becomes simple and does not require any prior setup. Interesting properties and limitations of our method are discussed based on experiments with the mobile robot Fetch.
