  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.
