ゼロショット能動視覚探索（ZAVIS）：ロボットアシスタントのための知的物体探索
Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants
自由形式の言語で指示された物体を移動ロボットが効率的に探索するため、常識知識に基づく共起と予測不確実性を考慮したセマンティックグリッドマップ計画手法を提案し、シミュレーションと実環境で有効性を示した。
著者: Jeongeun Park, Taerim Yoon, Jejoon Hong, Youngjae Yu, Matthew Pan, Sungjoon Choi
分類: cs.RO, cs.CV
原文アブストラクト
In this paper, we focus on the problem of efficiently locating a target object described with free-form language using a mobile robot equipped with vision sensors (e.g., an RGBD camera). Conventional active visual search predefines a set of objects to search for, rendering these techniques restrictive in practice. To provide added flexibility in active visual searching, we propose a system where a user can enter target commands using free-form language; we call this system Active Visual Search in the Wild (AVSW). AVSW detects and plans to search for a target object inputted by a user through a semantic grid map represented by static landmarks (e.g., desk or bed). For efficient planning of object search patterns, AVSW considers commonsense knowledge-based co-occurrence and predictive uncertainty while deciding which landmarks to visit first. We validate the proposed method with respect to SR (success rate) and SPL (success weighted by path length) in both simulated and real-world environments. The proposed method outperforms previous methods in terms of SPL in simulated scenarios with an average gap of 0.283. We further demonstrate AVSW with a Pioneer-3AT robot in real-world studies.
