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In this paper, we focus on the problem of efficiently locating a targetobject described with free-form language using a mobile robot equipped withvision sensors (e.g., an RGBD camera). Conventional active visual searchpredefines a set of objects to search for, rendering these techniquesrestrictive in practice. To provide added flexibility in active visualsearching, we propose a system where a user can enter target commands usingfree-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 userthrough a semantic grid map represented by static landmarks (e.g., desk orbed). For efficient planning of object search patterns, AVSW considerscommonsense knowledge-based co-occurrence and predictive uncertainty whiledeciding which landmarks to visit first. We validate the proposed method withrespect to SR (success rate) and SPL (success weighted by path length) in bothsimulated and real-world environments. The proposed method outperforms previousmethods in terms of SPL in simulated scenarios with an average gap of 0.283. Wefurther demonstrate AVSW with a Pioneer-3AT robot in real-world studies.

Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants